Deep Streaming View Clustering
Authors: Honglin Yuan
Xingfeng Li
Jian Dai
Xiaojian You
Yuan Sun
Zhenwen Ren
Venue: ICML 2025 poster (2025)
Keywords: Streaming view clustering
Multi-view clustering
Concept drift problem
Distributed consistency learning
Primary area: general_machine_learning->clustering
TL;DR: We propose a deep streaming view clustering method, which aims to mitigate the effect of concept drift between streaming views on clustering performance and achieve superior performance.

Abstract: Existing deep multi-view clustering methods have demonstrated excellent performance, which addressing issues such as missing views and view noise. But almost all existing methods are within a static framework, which assumes that all views have already been collected. However, in practical scenarios, new views are continuously collected over time, which forms the stream of views. Additionally, there exists the data imbalance of quality and distribution between different view streams, i.e., concept drift problem. To this end, we propose a novel Deep Streaming View Clustering (DSVC) method, which mitigates the impact of concept drift on streaming view clustering. Specifically, DSVC consists of a knowledge base and three core modules. Through the knowledge aggregation learning module, DSVC extracts representative features and prototype knowledge from the new view. Subsequently, the distribution consistency learning module aligns the prototype knowledge from the current view with the historical knowledge distribution to mitigate the impact of concept drift. Then, the knowledge guidance learning module leverages the prototype knowledge to guide the data distribution and enhance the clustering structure. Finally, the prototype knowledge from the current view is updated in the knowledge base to guide the learning of subsequent views. Extensive experiments demonstrate that, even in dynamic environments, the clustering performance of DSVC outperforms 12 state-of-the-art DMVC methods under static frameworks.

Deep Streaming View Clustering
Honglin Yuan Xingfeng Li
Jian D
Xiaojian You'
Yuan Sun
*45 Zhenwen Ren"
Abstract
1. Introduction
Existing deep multi-view clustering methods have demonstrated excellent performance, which ad- dressing issues such as missing views and view noise. However, almost all existing methods are within a static framework, which assumes that all views have already been collected. Neverthe- less, in practical scenarios, new views are con- tinuously collected over time, which forms the stream of views. Additionally, there exists the data imbalance of distribution between different view streams, i.e., concept drift problem. To this end, we propose novel Deep Streaming View Clustering (DSVC) method, which miti- gates the impact of concept drift on streaming view clustering (SVC). Specifically, DSVC con- sists of knowledge base and three core mod- ules. Through the knowledge aggregation Iearn- ing module, DSVC extracts representative fea- tures and prototype knowledge from the new view. Subsequently, the distribution consistency learn- ing module aligns the prototype knowledge from the current view with the historical knowledge dis- tribution to mitigate the impact of concept drift. Then, the knowledge guidance learning module leverages the prototype knowledge to guide the data distribution and enhance the clustering struc- ture. Finally, the prototype knowledge from the current view is updated in the knowledge base to guide the learning of subsequent views. Extensive experiments demonstrate that DSVC significantly outperforms state-of-the-an methods.
With the advancement of information technology, data can often be obtained from multiple sources and views, which results in multi-view data. As popular direction of unsuper- vised learning, multi-view clustering (MVC) aims to group data into distinct clusters by integrating information from different views, thereby uncovering shared clustering prop- erties of multi-view data (Qin et al., 2024; Sun et al., 2024b; Xu et al., 2022; Song et al., 2025; Sun et al., 2024d; Wong et al., 2023; Sun et al., 2024a). In contrast to single-view clustering, MVC can capture data information from mul- tiple perspectives, which provides understanding.
comprehensive
80
100
66.20
60.05
63.85
60
ACC
80
20
60
Ours
50-
3
View
Streaming View
(a) Imbalance distribution
(b) Clustering performance
Figure I. The illustration of the concept drift problem on the Hand- written dataset. (a) is the clustering performance of each view. Due to distribution imbalance across different views, which leads to different views exhibiting varying performance. This indicates the presence of concept drift across different views. (b) is the experiment of view streaming increases. Due to concept drift prob- lem among different views, the clustering performance of LSVC exhibits continuous fluctuations as more views are incrementally collected. It shows that existing lifelong leamingg methods (LSVC) are unable to address the concept drift problem in SVC tasks.
*Corresponding author School of National Defense Science and Technology, Southwest University of Science and Technol- ogy, Mianyang, China "Scooo of Computer Science and Tech- nology.Southwest University of Science and Technology, Mi- anyang, China 'Southwest Automation Research Institute, Mi- anyang, China 'College of Computer Science, Sichuan University, Chengdu, China NNtiional Key Laboratory of Fundamental Algo- rithms and Models for Engineering Numerical Simulation, Sichuan University, Chengdu, China. Correspondence to: Yuan Sun <sun- york_work@ @ 163. com>, Zhenwen Ren <rzw @ @wwuu@juuuuuuun...
Over the last decade, researchers have proposed a signifi- cant number of MVC methods. Traditional MVC methods can generally be classified into four main types, i.e., non- negative matrix factorization (NMF) (Feng al., 2024: Li et al., 2023b), graph-based learning (Du et al., 2023; Liang et al., 2024; Liu et al., 2023; Li et al., 2025), multi-kerne- based (Xu et al., 2024b: Li et al., 2022), and subspace-bar methods (Li et al., 2024a; Sun et al., 2021). Thanks to the powerful nonlinear representation capabilities of deep learn-
Proceedings of the nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Deep Streaming View Clustering
ing, a large number of deep multi-view clustering (DMVC) (Bian al., 2024; Chen et al., 2024; Yuan al., 2024; Fu et al., 2025a; Yuan et al., 2025) methods have been proposed in recent years, which have gradually emerged as main- stream research direction. Although existing MVC methods have demonstrated excellent clustering performance within static frameworks, they often overlook the treatment of real- time data. In dynamic task environments, the new view will continuously accumulate over time, i.e., streaming view data. In this context, the model should be updated and trained on new data promptly, rather than waiting for the complete collection of all view data before training. This limitation poses a significant challenge to the processing of streaming view data.
could be the first work to reveal and study the issue of concept drift in the context of streaming view cluster- ing.
To mitigate the effect of concept drift, we propose a dis- tributional consistency learning module that aligns the prototype knowledge of the current view with histori- cal knowledge distribution, with the aim of improving the consistency of the collected data distribution.
We propose knowledge guidance learning module, which leverages prototype knowledge to guide data distribution and enhance the clustering structure of the feature representation.
To tackle this challenge, LSVC (Li et al., 2024b) employs the lifelong learning paradigm, which utilizes consensus bipartite graph to align the current view knowledge with the historical knowledge, and fuse the current view knowledge into the historical knowledge, thereby enabling streaming view clustering (SVC). However, since data from different views originate from distinct sensors or angles, there ex- ists an imbalance situation in the distribution of the data across view streams, such situation is known concept drift (Sharief et al., 2024; Jiao et al., 2022), shown in F Due to the issue of concept drift, the outdated model struggles to adapt to the training of new view data, thereby leading to a significant decline in clustering perfor- mance. Specifically, as illustrated in Fig. LSVC places excessive emphasis on preserving historical knowledge, in which outdated information can mislead model training, making it difficult to adapt to and effectively process new view data. Therefore, it is essential to overcome concept drift and ensure the collected data exhibits a unified feature representation and distribution.
Experimental results on eight datasets demonstrate that our proposed DSVC significantly outperforms 13 state- of of-the-art MVC methods, highlighting its effectiveness in real-world streaming view scenarios.
2. Related Work
2.1. Deep Multi-view Clustering
Due to the powerful nonlinear representation capabilities of deep neural networks, many deep multi-view clustering (DMVC) algorithms (Fu et al., 2025b; Cui et al., 2024; Sun et al., 2024c) have been proposed in recent years. Existing DMVC methods primarily address three key challenges: the partial view misalignment problem, the partial view miss- ing problem, and the noisy view problem. Specifically, to address the issue of misaligned views in multi-view data, DeAIMVC (Yang et al., 2023) introduces dual-contrast alignment network, which promotes consistency between paired view features by integrating both global and local alignment losses. SURE (Yang et al., 2022) introduces a noise-robust contrastive loss, which effectively mitigates the effect of false negatives due to random sampling. To ad- dress the problem of missing partial views, Prolmp (Li et al., 2023a) and CPSPAN (Jin et al., 2023) leverage the relation- ship between prototypes and samples to infer missing data. To address the challenge of noisy data in the views, MVCAN (Xu et al., 2024a) mitigates the side effects of noisy views, which by supporting the non-shared parameters for multiple views and inconsistent clustering predictions. RMCNC (Sun et al., 2024c) introduces a noise-tolerance multi-view con- trastive loss that avoids overemphasizing noisy data, thereby alleviating issues associated with data noise. However, in practical scenarios, multi-view data is often collected in the form of streaming views. Existing deep learning methods fail to account for this dynamic setting. To address this limitation, we propose a deep streaming view clustering (DSVC) method tailored for dynamic clustering tasks in real-world applications.
In this paper, we propose a novel deep streaming view clus- tering (DSVC) method, as illustrated in 2 DSVC con- sists of knowledge base and three core modules: the knowl- edge aggregation learning module (KAL), the distribution consistency learning module (DCL), and the knowledge guidance learning module (KGL). Specifically, when new view arrives, we utilize the KAL module to aggregate repre- sentative prototype knowledge and feature representations from the current view. Then, the DCL module employs distribution consistency learning loss to align prototype knowledge of the current view with historical knowledge, which promotes distribution consistency of collected data. Subsequently, the KGL module leverages prototype knowl- edge to enhance the clustering structure of the features. In general, the main contributions of our work are as follows:
We propose a novel deep streaming multi-view cluster- ing framework named Deep Streaming View Cluster- ing (DSVC). To the best of our knowledge, our DSVC
2

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Deep Streaming View Clustering
view
Blstributian
Knowkedgy
knowledgy Base
Lad view
Classeriigl
Update
Updatic
Knowledge Aggregatioon Learning
Kaowledgs Guidanee Tearning
Kaowleder Aguragatine Learning
Kamnledg Leanting
H
Keen www.eadeennooo Apprepate Laur wade
Krondedgg Rasn
Bbiribution Camistoncy Leoning
Streaming View
Data
Prinetpe Krondedge
Defloree trisappe Kownlag le
Semenng Vice
Pud
N11
Figure 2. The framework of the proposed DSVC. We employ the knowledge aggregation learning module to extract prototype knowledge feature representations from the current view. The distribution consistency learning module is utilized to align the knowledge distribution of the current view with that of the historical knowledge, thereby mitigating the influence of concept drift. Furthermore, the knowledge guidance learning module is introduced utilize prototype knowledge guide data distribution and strengthen the clustering structure. Finally, the prototype knowledge from the current view is updated into the knowledge base, which guides the training of subsequent views.
2.2. Stream Learning
3. Method
Streaming learning (also called continual learning or incre- mental learning) is machine learning paradigm, which aims to process data points or data blocks that arrive se- quentially over time. Existing streaming learning scenarios include task-stream learning, domain-stream learning, and class-stream learning- Specifically, task-stream learning (Oren & Wolf, 2021; Jiang & Celiktutan, 2023; Li al., 2024c) aims to process tasks that arrive sequentially, where the new tasks significantly differ from the histori- cal ones. In domain-stream learning (Wang et al., 2024; Shi & Wang, 2023; Pei et al., 2024), the sample categories across different domains remain the same, but the input distributions are different. In class-stream learning (Zhou al., 2024b;a; Huang et al., 2024), the model is required continuously distinguish between newly arrived classes and previously learned classes. Overall, existing streaming learning methods overlook the following issues: (1) With the development of multi-view learning, data often contin- uously accumulated in the form view streams. However, existing methods rarely take this scenario into account. (2) Due the fact that data from different views originate from different sensors or views, there is situation of imbalance and instability in the distribution of data across view streams (i.e., concept drift), which presents new challenge for the adaptability ability of the model to deal with the new view data. For those, we propose novel DSVC method to enable streaming view clustering and mitigate the effect of concept drift on model performance.
3.1. Notation
We assume that the dataset {X+) V views, with the dimensionality of D and N samples. During training, the V views arrive sequentially, and data from views where < is not accessible to the model dur- ing the training of the e-th view X" = -xx ......,,, .,y E R-V x Z" E R Nxd denotes the feature representation, where is the feature dimensionality. H" E N repre- sents the reconstructed feature representation. P R Rxd refers the prototype knowledge of view e in X", and K indicates the number of prototype knowledge.
NXD consists of
3.2. The Objective Function
The overall architecture of our proposed method is illus- trated in Fig.2. When collecting the first view data, the knowledge aggregation learning (KAL) module preserves as much view information as possible through reconstrue- tion loss. then leverages an attention mechanism to re- construct features and prototype knowledge. However, at this stage, the knowledge base empty and cannot be used to constrain the knowledge distribution of the current view. To ensure that the reconstructed features remain aligned with the original data distribution, we employ the distri- bution consistency learning (DCL) module. This module promotes distribution consistency between the latent fea- tures and the reconstructed features, thereby enhancing the representativeness of the prototype knowledge learned from the first view. addition, we leverage the knowledge guid- ance learning (KGL) module guide the data distribution, thereby strengthening the clustering structure. Finally, the
3

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Deep Streaming View Clustering
prototype knowledge from the first view is updated into the knowledge base. Consequently, the total loss for the first view is formulated as:
cally, the cross-attention A is calculated follows:
A " SSotmmx (WI
(4)
a La + B.g
where W and W represent the linear layers for the la- tent features and prototype knowledge in the e-th view, respectively. Subsequently,
When the number of collected views exceeds one, we con- tinue to utilize the KAL module to extract feature repre- sentations and prototype knowledge. However, because of concept drift exists between view streams, which makes it difficult for previous models to adapt to the training re- quirements of subsequent views, as shown in (ib. To address this issue, the distribution consistency learning mod- ule aligns the prototype knowledge of the current view with the knowledge base, which mitigates the effect of concept drift. Additionally, we leverage the KGL module to enhance the clustering structure. Finally, the prototype knowledge of the current view is updated into the knowledge base to guide the training of subsequent views. Overall, the total loss is defined as:
the attention A to re- aggregate prototype knowledge P RNNK from the la- tent features, and reconstruct the features H E R xxd from the prototype knowledge. Mathematically. it can be expressed as follows:
P = U + + Z = + +2'"+A'WW" A"W"UU
where W and W z represent additional linear layers for the prototype knowledge and latent features, respectively.
3.4. Distribution Consistency Learning
During the data collection process, there exists the concept drift between the stream views. To this end, we propose distribution consistency learning strategy that aligns the knowledge of the current view with historical knowledge
C C= + La BL.
(2)
where C, represents the reconstruction loss, La represents the distribution consistency learning loss, and Lg represents the knowledge guidance learning loss. and B are trade-off parameters.
B = {b,, b2,..., bx} RKxd thereby promoting the consistency of the collected data distribution. Specifically, cosine similarity evaluate the correlation between current view knowledge and historical knowledge. The formula is defined as follows:
3.3. Knowledge Aggregation Learning
5(pj.b, = (b,b,)²
(6)
The collected data often contains substantial redundant infor- mation. To obtain flusery feature representations, we employ the autoencoder to extract the latent feature representations of the data. Specifically, we employ the encoder fi to extract features Z" R from the data X R N= Then, the decoder g7 reconstructs the data D" from the features Z". Mathematically, the
where p represents the i-th prototype knowledge of the current view, and by denotes the j-th knowledge of the historical view. Thus, the probability that the two types of knowledge belong to the same class in the historical view given by:
X" E N reconstruction loss is defined as:
exp (S(bi.pp)))) Σラ= exp (S(b,, p()/T) + Ejtr exp (S(p.,)) PTTT)
C, = X (5
(Tz. ([a((( (X
(3)
where т represents the temperature parameter, which is set to 1. Additionally, we normalize Q³ to ensure that i-i Σ Q = To mitigate concept drift and promote con-
where 8 and d² represent the network parameters of the encoder and decoder, respectively. To capture the com- monalities among the data and the unique characteristies between classes, we first utilize MLP to derive K prototype knowledge U = W(Z") E N from latent features Z, where Wrepresents the linear layer of the MLP network, K = M C with M N+ indicates the number of classes. However, the capability of the MLP depends its depth and width, which makes it challenging to extract high-quality shared knowledge from complex latent representations. Therefore, we employ the cross-attention mechanism to adjust the distribution among the data using prototype knowledge, and aggregate more dis- criminative prototype knowledge from the features. Specifi-
sistency sample distributions different view streams, we propose the distribution consistency learning loss, which is defined follows:
hyper-parameter and C'
Ls (KI((Q)1 DKI(Q"11Q") 22:2220
K
Q Q + Qt log o%
(8)
10 log Q
=
where DKL. represents the Kullback- divergence. The goal of La is to maximally align the knowledge dis- tribution between the current view and the historical view,
4

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Deep Streaming View Clustering
thereby unifying the data distribution. Moreover, when the first view is collected, the knowledge base is empty, therefore it cannot be used to constrain the knowledge distri- bution of the current view. To ensure that the reconstructed features do not deviate from the original data distribution, we utilize the loss defined in (8), which aims to promote consistency between the latent and reconstructed features. This helps to make the prototype knowledge learned from the first view more representative.
includes three views, ie., GIST, PHOG, and LBP. Scene-15 (Fei-Fei & Perona, 2005) dataset consists of 15 categories, including scenes such as office, kitchen, living room, and mountain, with a total of 4,485 samples. Each sample is represented by three distinct features, i.e., GIST, PHOG, and LBP. ALOI-100 (Geusebrock et al., 2005) consists of 10,800 object images, belonging to 100 classes. Multi-view data is constructed by extracting HSB, RGB, Colorsim, and Haralick features from these images, respectively. St110-fea (Coates et al., 2011) comprises 13,000 samples spanning 10 categories. Our experiments use its three distinct deep fea- tures as the different views. YoutubeFace (Wolf et al., 2011) consists of 101,499 samples across 31 classes. We utilize its Audio volume, Cuboids histogram, Vision HIST, HOG, and MISC features for experimental evaluation. ALO1-1000 (Geusebroek et al., 2005) comprises 110,250 object images, and 1,000 classes. Each sample has four features, i.e., HSB, RGB, Colorsim, and Haralick.
3.5. Knowledge Guidance Learning
To optimize the feature distribution and strengthen the clus- tering structure, use the aligned prototype knowledge to guide the data distribution of the current view. Specifically, we first compute the similarity between the features and the prototype knowledge. The formula is as follows:
M(H, ..)) = ( (SM). P))),
where hy represents the i-th feature of the current view, and pý denotes the j-th prototype knowledge. Thus, the probability that the j-th prototype knowledge contains the i-th feature is given by:
Table 1. Multi-view datasets in the experiment.
Datasets
Samples Clusters Views
Dimensionality
ALOI-10
1079
10
4
64/64/77/13
HandWritten
2000
10
6
216/76/64///40//47
LandUse-21
2100
21
3
20/59/40
exp(S(h), pz))
Scene-15
4485
15
3
20/59/40
((.))) Σ (1e
(10)
ALOI-110
10800
100
4
77/13/44/125
St110-fea
13000
10
3
1024/5 12/2048
YoutuheFace
101499
31
5.
64/5
1//512//64/44//888
where K is the number of prototype knowledge. To opti- mize the clustering structure of the features, our knowledge guidance learning loss is defined follows:
A001-1000
110250
1000
4
125/77/13/64
4.2. Comparison Methods
Lg KN
log G(J))
(11)
We compare DSVC with 12 state-of offee-of-the-ar DMVC methods and the sole SVC method to demonstrate its effectiveness. Specifically, these methods include: COMIC (Peng et al., 2019). DSRL (Wang et al., 2021), MFLVC (Xu et al., 2022). DCP (Lin et al., 2022), CVCL (Chen al., 2023), DealMVC (Yang et al., 2023), CPSPAN (Jin et al., 2023), DMCE (Zhao et al., 2023), GDMVC (Bai et al., 2024), MAGA (Bian et al., 2024), FMCSC (Chen et al., 2024), MVCAN (Xu et al., 2024a), and LSVC (Li et al., 2024b).
F=
where N is the number of features. Finally, we update the prototype knowledge of the current view into the knowledge base (i.e., B = Pd ), to guide the training of the subsequent views collected.
4. Experiments
4.1. Datasets
To comprehensively evaluate these comparison methods, we employ three widely used metrics: Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI).
We design series of experiments on eight widely used datasets, which encompass various data types, to demon- strate the effectiveness of our DSVC method. Detailed information for all datasets is provided in I. Concretely, ALOI-10 (Geusebrock et al., 2005) contains 1.079 samples across 10 categories, with each sample having four features, i..., HSB, RGB, Colorsim, and Haralick. HandWritten (LeCCun et al., 1989) consists of 2,100 samples, covering 10 categories corresponding to digits 1 1-9 9. Each sample is char- acterized by six types of features including Pixel, Fourier, Profile, Zer, Kar, and Mor. LandUse-21 (Yang & Newsam, 2010) contains 2, 100 satellite images across 21 categories. It
4.3. Experimental Settings
Our DSVC is implemented in PyTorch 2.3.0, and all experi- ments are performed GPU and 32GB RAM. For our DSVC, the autoencoder con- sists of fully connected network. The encoder ( decoder) network has the architecture of 00-512 1024 512-256 (256 512 - 1024 - 512 - D"), where D represents the feature dimension of each view stream. the experiments,
Linux system with NVIDIA
5

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Deep Streaming View Clustering
Table Performance comparison (mean + standard deviation) with four datasets, 'O/M' represents ou--o--mmmory. ' represents the streaming view clustering method, the optimal results are highlighted in bold red and the suboptimal results are shown in bold blue.
Datasets
HandWritten
ALOI-OO
Landuse 21
Scene- 15
Evaluation metries
ACC
NMI
ARI
ACC
NMI
ARI
ACC
NMI
ARI
ACC
NMI
ARI
COMICHIMLL199 19)
63.6010.43 62.9140.66
63.9120.36
56.23x2.62
60.43-2.40 66.5822.65
21.91a1.25 28.5810.92
89340.54
33.8920.64
40.5520.48
24.1620.91
DSRL(TPAMI'21)
83.7810.29 88. 38.2240.48
81.1720.64
78.7622.60
65.8321844
61.28s4.10
28.2350.75
28.7920061
13.0620.04
45.5320.86
40.11a0.54
24.0120.67
MFLVC(CVPR 22)
83.7424.09 80,83+2,49
81.95+4.02 02
63.6621001
67.23:2.61
60.27:1.65 65
19:90=0.28
22.33+0040
7.13+0.21
37.73=1.04
37,81+0.91
21.56t1 .05
DCP(TPAMI'22 22)
78. 18.1440.45 76.8640.24
68 68.65+0.52
64.5024.66
63.60=3.51
55.99+4.09 .0
28.29:1.36
32.49+1.20
14.01=0.67
42.27=3.23
41.05=2.66
23.23+2.06
CVCL(CCV'23)
84.55+9.43
87.2943.51
80 80.96+7.75
82.33+8.47
80.34:651
51
10.94
19:20+0.62 62
21 11.9540.23
6.43+0. 18
36, 15.1532.73
38.19H1.8
20.69+1.8 .884
DeaIMVC(MM'23)
80.69:0.48
80. 13+40.46
71.79+0.61
71.38+4.49
75.40=1.95 1.98
59.86-5.05 .5
19.01+0.80
16.75+1.24
13.50+0.49 49
35.79+0.94
36.59+0.81
21.97+0.83
CPSPANICVPR
85.7244.68
84.52+2.35
75.7743.62
67.8447.77
81.01=2.96
63.91=7.64
25.59+1.55
32.49H1135
12.00+0.67
38.78+2.44
37.71+2.23
21.59+2.17
DMCI(23'PR)
94.30:3.97
91.35a1.77
90.0643.91
61.33+2.06
11.04=L001
52.73=1.71
20.31.00700
28.1 28.19400:46
8.47+0.83
32.37=2.48
31.67#1.16
15.62+1.24
GDMVC(KBS
84.3240.34
38.43400.5
81.7840.63
85.6424.088
83.78&1.87
71887=4.83
27.21=1.38
33.4611611
12.3420.45
38.55&1.16
39.2140.38
20.3340.49
MAGA((F224)
92.22&1.04
84.9541.42
83.6342.07
57.4428.40
62.6426.61
43.57=8.50
19.4621101
20.18
6.31.0.55
32.21:0.78
34.50.0.75
17.6820.58
FMCSC(NeurIPS 24)
84.35+0.38
74.01+0.22
70.41+0.29
85.1743.82
83,98=1.05
77.53=2.29
21.66600336
23.25+0.53
8.20+0.26
34.29+2.67
31.57+2.08
18.23+11.99 .95
MVCAN(CVPR'24)
94. 1240.57
88.41+0.59
87.51=1.12
54.51#3.81
63.27=2.51
45.08=3.55
22.38% 28.94=1.50
9.45
37.76=1.06
39.18:0093
20.94a0.92
LSVCCINNLS'23)
88. 38.69A0.87
89.45400.9 81.47a0.77
66.90&4.01
67.81=3.43
60.53a4.12
24.66&1.44
26.71110.58
11.1320.67
40.68=1.26
37.54=1.31
23.98s .66
DSVC(Ours)
95.4420.32 90.2620.61 90.1620.68
86.6923.85
85.67=2.94
80.37+3.46
28.5221.16 33.0920074 14.1620..8
45.3320.88
43.13=1.13 27.61=1.10
Table Performance comparison (mean # standard deviation) with four datasets, 'O/M" represents out-of-memory. '*" represents the
streaming view clustering method, the optimal results are highlighted in bold red and the suboptimal results are shown in bold blue.
Datnsets
Sel1O-fen
ALOO--10 100
YoutabeFuce
AL.0--0000
Evaluation metrics
ACC
NMI
ARI
ACC
NMI
ARI
ACC
NMI
ARI
ACC
NMI
ARI
COMICICML' 19)
20.51#3.52 12.6643.07 15.33+2.40
59.19#1.93 59.8361444 54.6950.78
O/M
O/M
O/M
O/M
O/M
C/M
DSRL(TPAMM'21)
OIM
OM
O/M
O/M
O/M
O/M
0/M
MFLYCICVPR
35.9228.47
19.9129.28
14.8928.84 84
66.23+1.50 67.1421.33
56.77=1.09
24.54:1.11
21.62:104
4.5610.58
OIM
O/M
O/M
DCP(TPAME'2))
20.41=1997
14.4442.81
1.78
60.42:0.52
81.9880.61
51.12=0.49
21.92+1661
18.52+1.23
3.8420.54 48.1220.86 66.01=0.72 37.50=0.65
CVCLJCCV123)
20.68:0.54
14.84a0.24
22.82+1.56
60.2642.54
82.6340.67
54.67=1.50
19.74x3.27
19.8240.91
4.22+0.45
O/M
O/M
CVM
De
12.1420.33
14.2420.91
14.41=00.4
19.66a1.52
58.9240.51
12.1620.83
18.95a0.75
15.6440.74
3.32+0.50
O/M
CPSPAN(CVPR'23)
24.46110.59
15:47112.59
10.0249.24
66.77.2.05
83.6340.95
56.5922.99
20.6343.24
18.7613.19
4.02+1.19
43.81=2.61 76.12s1.37 30.6622882
DMCE(23
28.1424.02
19.5923.54
11.1423.03
74.83400.52
83.8240.27
62.20=0.50 50
O/M
O/M
O/M
O/M
O/M
O/M
GDMVCIKBS'24)
14.3350.69
14.43=0.65
6.8+1.32
80.15+0.66
86.82:0.26
64.13+0.85
O/M
O/M
O/M
O/M
O/M
O/M
24GA(F
56.37416.12
52,64:15.56
14.03+14.55
52.9840.91
69.57+0.57
40.45a48
22.95+1.36 23.68&1.21 3,77+0.37
O/M
O/M
O/M
FMCSC(NeurIPS' 24)
25.04+2663
17,52+3.26
28.95+2.36
36
17
65 65.90+0.64
57.46+0.12 12
22.46++.001
21. 10+0.89 4, 4.440525
O/M
O/M
O/M
MVCAN(CVPR 24)
48. 18.57+12.44
52.24=16.34
39.41+13.007
23.2344
82.96+0.42
55.66+0.42
12.7640.33
13.65+0.49 2.071
0/M
O/M
O/M
LSVCCTNNLS'24)
47.9826.11
51.0326.47
40.90244.16
64.11+0.66
74.15+0.48
55.01=1.49
O/M
O/M
O/M
0/M
O/M
O/M
DSVC(Ours)
63.41=996 62.66499004 53.58=9.72
80.53+0.59 89.01=0.18 73.96=0.82
25.09H1.09 24.45+0.85
5.60#0.41
58.32:0.56 81.20=0.24 44.93=0.49
Table 4. Ablation studies on eight datasets, where 'V' indicates the used component.
Datascts
ALOI-10
HandWritten
Landuse-21
Scenc-15
St1l0-fea
ALOI-100
YoutubeFace
ALOI-1000
La
C
ACC
NMI
ACC
NMI
ACC
NMI
ACC
NM1
ACC
NMI
ACC
NMI
ACC
NMI
ACC
NMI
69.05
74.90
81.75
81.94
18.00
28.73
30.77
33.30
38.32
34.48
58.70
81.27
18.37
19.60
36.71
73.57
74.42
80.84
86.45
78.29
24.86
32.23
39.60
40.68
52.65
58.36
79.05
88.5
23.50
23.04
54.13
76.35
V
76.92
80.08
88.33
86.70
24.75
29.86
40.02
41.50
53.75
52.66
77.33
87.16
23.07
23.04
56.00
79.95
78.96
80.25
90.95
83.44
24.62
31.67
35.72
36.93
45.28
38.53
54.43
71.16
18.45
19.81
40.42
71.40
86.69
85.67
95.44
90.26
28.52
33.09
45.33
43.13
63.41
62.66
80.53
89.01
25.09
24.24
58.32
81.20
we train each collected view for 200 epochs with batch size 256 and learning rate 0.0001. Additionally, we use Adam optimizer for model optimization and employ ReLU as the activation function.
uniformly set a and [3 to 0.1. To comprehensively evaluate our clustering performance, we tested all methods using five different random seeds and calculated the mean and standard deviation as the final results.
In DSVC, which included two adjustable parameters, i.e., o and B. For the HandWritten and Scene- 15 datasets, we set
4.4. Experimental Results Analysis
and 3 to and 0.1, respectively. For the Stal0-fea dataset, and 1 are set to 0.001 and 1. For all other datasets, we
Tabs.2 and 3 present the clustering results of DSVC com- pared to 12 state-of tot DMVC methods and the sole
6

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Deep Streaming View Clustering
LDVC
LMC -ur
55
75
65
ACC
70
15
15
62246678/11647
17/13/54/64
788161446620
77/44/13/64
02
21675846/24047
64/64/77:13
02
60
Streaming
Streaming
Stroaming View
Streaming
(a) Waittritten
(b) ALOI-10
(a) HandWrittte
(b) ALOI-10
-.-LSVC
77/64/13/125
1024/20245122
32
20
73133644125
2024/522/2048
45-
Streaming View
Streamingg Miew
Streaming Viek
Streaming View
(c) ALOI-100
(d) St10-fea
(c) ALOI-100
(d) St110-fea
Figure 3. The changes of clustering performance on four datasets, when view data continue to accumulate.
The clustering performance with different sequences of views streaming on four datasets
Figure 4.
SVC method across three evaluation metrics on eight dif- ferent datasets. From the tables, the following observations can be obtained:
As the scale of datasets continues to grow, LSVC and some DMVC methods unable handle these large- scale datasets within the constraints of limited computa- tional resources. For example, the ALOO- ALOII-100 dataset can only be processed by the two DMVC methods ( (i.e., CPSPAN and DCP) ) and our DSVC. However, our ap- proach is not only capable of handling these large-scale datasets, but also achieves optimal performance. This is because DSVC specifically designed to tackle the challenge of streaming view clustering in real-world tasks. Therefore, only view is considered at time during each training step instead of training on all views simultaneously. which significantly reduces the computational overhead.
Obviously, DSVC demonstrates superior perfor- mance compared to the other 12 state-of-the-art DMVC methods and the sole SVC method in nearly all sce- narios. Specifically, we take the ACC metric as an example, on the eight datasets (from ALOI-10 to ALOI- 1000), DSVC outperforms the suboptimal method by improvements of 1. 114. 1.05%, 0.23%, 3.06%. 7.04%, 0.38%, 0.55%, and 10.20%, respectively. This demon- strates that DSVC can extract representative infor- mation from the continuously evolving view streams, thereby uncovering more optimal clustering attributes.
4.5. View Stream Analysis
In addition achieving superior clustering perfor- mance, DSVC exhibits greater stability than subopti- mal methods, as evidenced by the lower standard devia- tion. This is because our knowledge aggregation leamn ing (KAL) module effectively extracts representative prototype knowledge and feature representations from the current view data. The distribution consistency learning (DCL) module aligns the prototype knowl- edge of the current view with the historical knowledge distribution, thereby mitigating the effect of concept drift. The knowledge guidance learning (KGL) module leverages aligned prototype knowledge to constrain the data distribution the current view, which enhances the clustering structure. The three modules work syn- ergistically to achieve the most stable performance.
To comprehensively analyze the performance of DSVC on dynamic tasks, present the clustering results after pro- cessing new views, as well as its performance across dif- ferent view streaming sequences. Specifically, as shown in Fig.3, with the continuous accumulation of new views, LSVC exhibits unstable performance, while the perfor- mance of our method is persistent increase. This is at- tributed the problem of concept drift across different view streams, which results in the previous model being unsuitable for training the next view. In contrast, our DSVC aligns the prototype knowledge of the current view with the historical knowledge by DCL, which unifies the data distribution across different view streams and effectively mitigates the concept drift problem. Compared DMVC
7

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Deep Streaming View Clustering
300
100
100
100
90
AcC
ACC
00
85
02
75
10
70
(a) Waittritten
(b) ALOI-10
(a) HandWritten
ALOI-10
60
8
50
ACC
10
ACC
8
4.
8
(c) ALOI-100
(d) St11O-fea
(c) ALOI-100
(d) St11O-fea
Figure 5. The clustering performance with different prototype knowledge numbers on four datasets
Figure Parameter sensitivity analysis on eight datasets.
prototype knowledge may cause the model overlook the cluster-specifie information, thereby failing to capture the commonalities within the same cluster. Based on experi- mental results, we set M to 7 for HandWritten dataset, while for other datasets, we set M to 1. For more experimental results, please refer to Section of the appendix.
methods, our DSVC consistently achieves near-optimal per- formance. This due to the KGL,, which leverages proto- type knowledge to guide the data distribution and enhance the clustering structure.
Additionally, shown in Fig.4, the initial performance gap is quite significant when processing view streams of different sequences. However, as the views accumulate, the performance gap between different sequences gradually decreases. This because the initially collected views exhibit significant differences in data distribution, which leads a noticeable performance gap. As more views are accumulated, our DCL module progressively aligns the data distribution, while the KGL module continuously enhances the clustering structure. As result, the final performance tends to be stable and consistent. For more experiments on view flow analysis, please refer to Section of the appendix.
4.7. Ablation Study
Our DSVC consists of three components: the knowledge aggregation learning module, the distribution consistency learning, and knowledge guidance learning module. To assess the effectiveness each component, we performed ablation studies four versions of DSVC across eight dif- ferent datasets. As shown in Tab.4, the removal of any com- ponent results in suboptimal performance. DSVC achieves optimal performance only when all three loss functions are included. The results demonstrate that the L, loss pre- serves the representative features of the data, maintaining its integrity and fidelity. The L, loss aligns the prototype knowledge distribution of the current view with the histori- cal knowledge distribution, effectively mitigating the impact of concept drift. The T.g loss leverages prototype knowledge to guide the data distribution of the current view, thereby enhancing the clustering structure.
4.6. Prototype Knowledge Number Analysis
To evaluate the impact of the number of prototype knowl- edge on the clustering performance of the model, we con- duct the experiment with prototype knowledge numbers ranging from shown in Fig.5, the clustering performance can be en- hanced with an appropriate amount of prototype knowledge. Both excessive and insufficient prototype knowledge nega- tively impact performance. Specifically, when the prototype knowledge is too few, the model tends to overly emphasize the commonalities between data, and neglect the distinc- tions within clusters. Conversely, an excessive amount of
M where M 1, 10. As
4.8. Parameter Sensitivity Analysis
DSVC incorporates two trade-off parameters, and B, which respectively regulate the reconstruction loss and the knowledge guidance learning loss. To evaluate the effec- tiveness the trade-off parameters, we varied their values
8

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Deep Streaming View Clustering
(a) 1-st view
(b) 2-nd view
(c) 3-rd view
(d) 4-th view
(e) 5-th view
(1) 6-th view
Figure The visualization results of our DSVC on the HandWritten dataset as the view accumulates.
(a) DMCE
(b) MAGA
(c) MVCAN
(d) Our DSVC
Figure 8. The t-SNE visualization of representations learned by DMCE, MAGA. MVCAN, and our DSVC on the HandWritten dataset.
within the range of 10a to 10 The experimental results, as illustrated in Fig.6, indicate that excessively high or low parameter values adversely impact clustering performance. This highlights the importance of balancing three losses within our model. Based on our experimental results, we recommend setting the parameter range between 10' to 10-1, For more parameter analysis, see Section D of the appendix.
tative features and prototype knowledge from the current view data. Subsequently, to mitigate the effect of concept drift, we leverage the distribution consistency learning mod- ule to align the prototype knowledge of the current view with the historical knowledge distribution, to enhance the consistency of the distribution across different view streams. Furthermore, the knowledge guidance learning module is introduced utilize prototype knowledge guide data dis- tribution, and strengthen the clustering structure. Finally, the prototype knowledge from the current view is updated into the knowledge base, which guides the training of subsequent views. Extensive experiments demonstrate the superiority and effectiveness of DSVC.
4.9. Visualization
To provide a more intuitive demonstration of the effective- ness of DSVC, conduct visualization experiments the Handwritten dataset. As shown in Fig.7, in the dy- namic task, with the accumulation of views, the boundaries between clusters become increasingly distinct, and the intra- cluster structures become more compact. As illustrated in Fig.8, even when compared to DMVC methods within a static framework, our DSVC exhibits superior inter-cluster separability and intra-cluster compactness. This demon- strates that DSVC not only tackles the problem of concept drift between different view streams, but also enhances the clustering structure by leveraging the prototype knowledge of the current view to guide the distribution view data.
A Acknowledgmentss
This work was supported by the Sichuan Science and Technology Program (Grant nos. 20252NSFSC0474, 2024ZDZX0004 ). the Mianyang Science and Technology Program (Grant nos. 2023ZYDF09 2023ZYDF003 ), the Sichuan Science and Technology Miaozi Program (Grant nos. MZGC20240057, MZGC2024014MM and the Postgrad- uate Innovation Fund Project by Southwest University of Science and Technology (Grant no. 24ycx1010).
5. Conclusion
Impact Statement
In this paper, we propose deep streaming view clustering (DSVC) method designed to address the challenges of con- cept drift and newly incoming view clustering in dynamic environments. To address these challenges, we employ the knowledge aggregation learning module to extract represen-
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none of which we feel must be specifically highlighted here.

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Deep Streaming View Clustering
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This document provides additional details and experimental results support the main submission. We first provide the pseudocode for DSVC in Section A. Then, Section B presents the experimental results of the view stream analysis. In Section C, we present the experiments analyzing the impact of the number of prototype knowledge. In Section D, we conduct a sensitivity analysis of the loss function parameters. Finally, in Section E, we discuss the limitations of our work.
A. DSVC Training Algorithm
In this section, we present the main algorithmic process of DSVC, as shown in Algorithm 1.
Algorithm The algorithm of DSVC
1: Input: New streaming view data X with size N; Batch size S; Training epoch E; The number of prototype knowledge
is K.
2: Parameter: a and B.
3: if then
4: while epoch < E do
5: for i= 1 to N/S do
6: Training the network by Eq.(3).
7: Aggregate prototype knowledge p and features H via Eq. (5).
8: Optimize feature H distribution via (8).
Leverages prototype knowledge p to guide the feature HD distribution via Eq.(1 10: end for
9:
Update prototype knowledge P into knowledge base B. end while
13:
Perform K-means algorithm on H
14: end if
15:
if > then
16: while epoch < E do
17: for == to N/S do
Training the network by Eq.(3).
Aggregate prototype knowledge P and features H via Eq. (5).
20:
Align the current view prototype knowledge P with the knowledge base B distribution via Eq. (8).
21:
Leverages prototype knowledge p to guide the feature H distribution via Eq.(1
22:
end for
23:
Update prototype knowledge pd into knowledge base B.
24:
end while
25:
Concatenate H to H
Perform K-means algorithm.
27:
end if
B. View Stream Analysis
In this section, we present additional experimental results regarding the continuous accumulation of views and the impact of different view streaming orders on performance, as shown Figs.9 and 10. We can see from the Fig.9, that LSVC not only fails to address concept drift problem but also cannot handle large-scale data. In contrast, our DSVC effectively mitigates concept drift while supporting the processing of large-scale data. This is achieved through our distribution consistency learning module, which aligns the knowledge distribution of the current view with historical knowledge, thereby alleviating the impact of concept drift. Additionally, shown in Fig. 10, the initial performance gap is quite significant when processing view streams of different sequences. However, as the views accumulate, the performance gap between different sequences gradually decreases. This is because the initially collected views exhibit significant differences in data distribution, which leads to a noticeable performance gap. As more views are accumulated, our distribution consistency learning module progressively aligns the data distribution, while the knowledge guidance learning module continuously enhances the clustering structure. As result, the final performance tends to be stable and consistent.
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Figure The changes of clustering performance on four datasets, when view data continue accumulate.
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Figure 10. The clustering performance with different sequences of views streaming on four datasets
C. Prototype Knowledge Number Analysis
Here more experimental results the effect of different numbers of prototype knowledge the performance of the model. we conduct the experiment with prototype knowledge numbers ranging from K to M K, where M = 2, 10. As shown in Fig. 11, the clustering performance can be enhanced with an appropriate amount of prototype knowledge. Both excessive and insufficient prototype knowledge negatively impact performance. Specifically, when the prototype knowledge is too few, the model tends to overly emphasize the commonalities between data, and neglect the distinctions within clusters. Conversely, an excessive amount of prototype knowledge may cause the model to overlook the -peciff- information, thereby failing to capture the commonalities within the same cluster.
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Figure 11. The clustering performance with different prototype knowledge numbers four datasets
D. Parameter Sensitivity Analysis
Here more experimental results the effect of different parameters. DSVC incorporates two trade-off parameters, and B, which respectively regulate the reconstruction loss and the knowledge guidance learning loss. To evaluate the effectiveness of the trade-off parameters, we varied their values within the range of 10 to 10-4, The experimental results, as illustrated 12, indicate that excessively high or low parameter values adversely impact clustering performance. This highlights the importance of balancing three losses within our model. Based on our experimental results, we recommend setting the parameter range between 10a 10-1
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Deep Streaming View Clustering
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Figure 12. Parameter sensitivity analysis on eight datasets.
E. Limitations
This paper proposes deep streaming view clustering (DSVC) method. To the best of our knowledge, DSVC the first work address stream view clustering within the deep learning framework, demonstrating superior performance even compared to DMVC methods under static frameworks. However, only consider the scenario where concept drift exists between view streams, without considering the potential issue of noisy data that may occur in real-world data collection. In the future, we will focus on addressing and improving this issue.
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Reviews:
[{"review_id": "njSTh0RJ0m", "reviewer": "Reviewer_oVPf", "summary": "This paper explores a rarely addressed area in multi-view clustering, namely, streaming view clustering. In this work, the authors utilize the Knowledge Aggregation Learning (KAL) module to extract features and prototype knowledge. Subsequently, the Distribution Consistency Learning (DCL) module is employed to mitigate the concept drift problem across view streams. Finally, the Knowledge Guidance Learning (KGL) module is introduced to enhance the clustering structure. Extensive experiments demonstrate the effectiveness of the proposed method.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Firstly, we sincerely thank you for your detailed review and constructive feedback, which have greatly contributed to improving the presentation of our submission.\n\n**S1: For instance, in Eq. 4, what does the symbol d represent?**\n\n**AS1:** Thank you very much for your valuable suggestions. In Eq. (4), $d$ represents the dimensionality of the feature $Z$.\n\n**S2:The authors are advised to ensure consistent use of capitalization throughout the manuscript.**\n\n**AS2:** We have already carefully reviewed the entire paper for consistency in terminology to ensure that the final version does not have such problems.\n\n**Q1: Could you clarify the distinction between domain streams and view streams?**\n\n**AQ1:** **Domain Stream** means that when a learner faces a series of tasks, the data input distribution (domain) for each task may be different, but the set of categories for the tasks remains the same. **View Stream** refers to a scenario where multiple views of the same object arrive sequentially in a streaming manner, one after another. The key distinction between the two lies in the difference of their task: **Domain Stream** primarily tackles domain drift, continual adaptation, and mitigating catastrophic forgetting while maintaining task consistency. In contrast, **View Stream** focuses on addressing challenges such as collaborative learning across multiple views and effectively handling view heterogeneity.\n\n**Q2: What are the advantages of the cross-attention mechanism compared to using prototypes learned through k-means clustering?**\n\n**AQ2:** \n\n**Advantage 1:** **$k$-means** partitions cluster centers based on a fixed distance metric. Its prototypes (i.e., cluster centers) are static and cannot dynamically adjust their distribution according to the data characteristics. In contrast, the prototypes learned through our **cross-attention mechanism** can dynamically adjust based on the data distribution, allowing the prototype knowledge to better fit with the data and become more representative.\n\n**Advantage 2:** The prototypes learned by **$k$-means** are susceptible to the influence of outliers (or noise). In contrast, the **cross-attention mechanism**, through the collaborative optimization between features and prototype knowledge, ensures that the learned prototypes are less affected by outliers (or noise), thereby enhancing robustness.\n\n**Q3: We observe that the stream-view clustering method LSVC also struggles with large-scale data, but the authors do not provide an explanation for this phenomenon.**\n\n**AQ3:** Thank you for your constructive feedback. Our DSVC leverages a prototype knowledge base to transfer historical knowledge, considering only prototypes and current-view samples during computation. This results in a computational complexity of $O(K \\times n)$, where $K$ is the number of prototypes and $n$ is the number of samples ($(K \\ll n$). Moreover, the memory overhead during updates is significantly reduced to $O(K \\times d)$, where $d$ denotes the embedding dimension. In contrast, LSVC exhibits a computational complexity of $O(n^2)$ and maintains historical knowledge via a consensus bipartite graph $\\mathbf{Z} \\in \\mathbb{R}^{k \\times n}$, requiring a storage overhead of $O(k \\times n)$. As the data scale increases, the computation and update of $\\mathbf{Z}$ lead to a substantial surge in memory consumption. Moreover, LSVC is a traditional shallow method, whereas DSVC is a deep learning-based approach. As a result, DSVC is well-suited for handling large-scale datasets, while LSVC struggles to cope with such scenarios.\n\n**Q4: During clustering, are the features used those derived from attention-based reconstruction, or the features extracted by the autoencoder?**\n\n**AQ4:** We perform clustering using the features $ H $ reconstructed through the cross-attention mechanism.\n\n**Q5. Fig. 11, it observe that in some datasets, the parameters $\\alpha$ and $\\beta$ have a significant impact on the results.**\n\n**AQ5:** Our method exhibits a certain degree of sensitivity to the parameters $ \\alpha $ and $ \\beta $. To analyze this effect, we conducted a sensitivity analysis, as shown in Fig.11. The results indicate that excessively high or low parameter values adversely impact clustering performance. This highlights the importance of balancing three losses within our model. In our approach, for the STL10-fea dataset, we set $ \\alpha $ and $ \\beta $ to 0.001 and 1, respectively, and set to 0.1 for both $ \\alpha $ and $ \\beta $ for the ALOI-1000 and ALOI-100 datasets. For the remaining datasets, $ \\alpha $ and $ \\beta $ are set to 0.1 and 1, respectively.", "claims_and_evidence": "Yes, the superiority and effectiveness of the proposed method are supported by extensive experimental evidence.", "methods_and_evaluation_criteria": "Yes, the proposed method effectively addresses the concept drift issue between view streams. The benchmark datasets (e.g., ALOI-10, HandWritten, LandUse-21, etc.) and performance metrics (ACC, NMI, ARI) used are appropriate for evaluating the effectiveness of the method.", "theoretical_claims": "Yes, in this paper, the necessity of addressing the concept drift problem in stream-based view clustering is thoroughly supported by relevant literature and experimental evidence provided by the authors.", "experimental_designs_or_analyses": "Yes, the paper conducts extensive experiments to demonstrate the effectiveness of the proposed method. By comparing it with state-of-the-art DMVC and SVC methods, the superiority of its performance is highlighted. The effectiveness of the method in mitigating concept drift is further validated through experiments with varying view stream sequences. Additional ablation studies also substantiate the rationality and effectiveness of each component.", "relation_to_broader_scientific_literature": "This study considers the scenario of data acquisition via view streams under dynamic conditions and compares the proposed method with existing DMVC and SVC approaches. It highlights the gap in the field of streaming view clustering and introduces a novel DSVC method to address these issues.", "essential_references_not_discussed": "In this paper, each theory and technique is supported by relevant references.", "other_strengths_and_weaknesses": "This paper demonstrates strong originality by identifying the issue of concept drift in the context of view streams. The proposed method is novel, with the distribution consistency learning module effectively mitigating concept drift between view streams. Comprehensive experiments are conducted to validate the superiority and effectiveness of the proposed approach. However, some deeper explanations are lacking in both the theoretical interpretation and experimental analysis. Further in-depth theoretical and experimental exploration would benefit the paper.", "other_comments_or_suggestions": "1) The authors should clarify the meaning of each symbol used in the manuscript. For instance, in Eq. 4, what does the symbol $d$ represent? Is it distance or dimensionality?\n2) The authors are advised to ensure consistent use of capitalization throughout the manuscript. For example, in the first paragraph of Section F in the appendix, $Loss$ is capitalized, whereas it is written in lowercase in other sections.", "questions_for_authors": "Q1: As far as I know, domain streams and view streams share conceptual similarities, and the authors have mentioned domain stream learning in related work. Could you clarify the distinction between domain streams and view streams?\nQ2: In Section 3.3, the authors employ a cross-attention mechanism to learn prototypes. What are the advantages of this approach compared to using prototypes learned through k-means clustering?\nQ3: In the last paragraph of Section 4.4, the authors mention that some DMVC methods cannot handle large-scale data under limited computational resources.  However, we observe that the stream-view clustering method LSVC also struggles with large-scale data, but the authors do not provide an explanation for this phenomenon.\nQ4: During clustering, are the features used those derived from attention-based reconstruction, or the features extracted by the autoencoder?\nQ5: Based on Figure 11, we observe that in some datasets (such as Stl10-fea and YoutubeFace-sel-fea), the parameters $\\alpha$ and $\\beta$ have a significant impact on the results. Could the authors provide an explanation for why this phenomenon occurs?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wh856wJtrK", "reviewer": "Reviewer_Rmer", "summary": "The paper presents Deep Streaming View Clustering (DSVC), a method designed to tackle concept drift in multi-view clustering with streaming data. DSVC features three modules: Knowledge Aggregation Learning (KAL) for feature extraction, Distribution Consistency Learning (DCL) to align current and historical knowledge, and Knowledge Guidance Learning (KGL) to enhance clustering. It outperforms 12 state-of-the-art methods in clustering accuracy, stability, and scalability on various datasets, making it effective for real-world streaming data applications.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the time you have taken to review our submission and provide valuable feedback. \n\n**Q1: Claims And Evidence**\n\n**Q1(a): Figure 1(a) needs more detailed explanation about the model and evaluation settings.** \n\n**AQ1(a):** Figure 1 (a) illustrates that due to distribution imbalance across different views, i.e., the concept drift problem, the performance of different views also varies.\n\n**Q1(b): How does this concept drift problem affect the performance?** \n\n**AQ1(b):** Thank you for your thoughtful comments! Since multi-view data are collected from different sensors or captured from various views, discrepancies in distribution and quality naturally arise among different views. This phenomenon is referred to as the concept drift problem. Existing multi-view clustering methods assume that all views are pre-collected for joint training simultaneously. However, in dynamic environments, due to the mechanisms of data generation, sampling frequencies and transmission speeds vary across different views. As a result, multi-view data is typically collected in a sequential manner (i.e., streaming view), which makes it infeasible to wait until all the views are ready for training. Therefore, data must be processed sequentially in a streaming fashion, training one view at a time. However, due to the presence of concept drift across different view streams, the similarity relationships between different samples and the feature representations of the same sample may vary across views after model training, which is shown in the following image (The image is in the anonymous URL: https://anonymous.4open.science/r/Fig-0D5B). This discrepancy reduces the intra-class feature consistency and the inter-class feature discriminability, thereby degrading clustering performance.\n\n**Q2: Theoretical Claims: The probability $Q_i^b$ cannot sum up to 1 if Eq.7 exists, and the authors do not explain why this term exists.**\n\n**AQ2:** Thank you very much for your insightful comment! The derived $Q_i^b$ from Eq. 7 does not sum to 1. Therefore, we apply the normalization to ensure its sum is strictly 1, making it suitable for the computation of $\\mathcal{L}_{d}$. Eq. 7 computes the distribution probability of class-specific prototypes in both historical knowledge and the current view. The complete formulation is as follows:\n\n$$\nQ_i^b =\\frac{\\exp{(S(p_i^v,b_i)/\\tau)}}{\\sum_{j=1}^{K}\\exp{(S(p_i^v,b_i)/\\tau)}+{\\sum_{j\\neq i}^{K}\\exp{(S(b_i,b_j)/\\tau)}}},Q_i^p =\\frac{\\exp{(S(b_i,p_i^v)/\\tau)}}{\\sum_{j=1}^{K}\\exp{(S(b_i,p_i^v)/\\tau)}+{\\sum_{j\\neq i}^{K}\\exp{(S(p_i,p_j)/\\tau)}}}.\n$$\n\nTherefore, $ Q_i^b $ can be interpreted as the similarity probability of the $ i $-th prototype knowledge ($ b_i $ and $ p_i^v $) mapped within the historical knowledge base ($ B $). Similarly, $ Q_i^p $ represents the similarity probability of $ b_i $ and $ p_i^v $ mapped within the current view knowledge. We achieve alignment of the current view prototype knowledge with the historical knowledge distribution by minimizing the discrepancy between the $ Q_i^b $ and $ Q_i^p $ distributions.\n\n**W1: The authors should include the notation of Z and H in Figure 2 for better understanding.** \n\n**AW1:** We have optimized Figure 2 accordingly in the revised manuscript.\n\n**W2: The authors claim they might be the first work to reveal the issue of concept drift in the context of streaming view clustering, but the paper lacks thorough investigation to address the problem.**\n\n**AW2:** Through a comprehensive survey and review of related literature, the most comparable methods to our setup are CAC [1], ACMVC [2], LSVC [3], and OBAL [4]. Among them, CAC and LSVC achieve streaming view clustering by maintaining a consensus matrix, while ACMVC and OBAL consider data stream scenarios. However, none of these methods account for the concept drift problem in streaming views, highlighting the novelty of our proposed DSVC. Furthermore, we have conducted a more comprehensive literature analysis to enrich the manuscript. We look forward to further discussions with you.\n\n**Reference：** \n\n[1] Live and learn: Continual action clustering with incremental views, AAAI, 2024.\n\n[2] Continual multi-view clustering with consistent anchor guidance, IJCAI 2024\n\n[3] LSVC: A Lifelong Learning Approach for Stream-View Clustering, TNNLS, 2024.\n\n[4] Online boosting adaptive learning under concept drift for multistream classification, AAAI, 2024.\n\n**W3: The dimensions of P and B need to be clarified.**\n\n**AW3:** We have revised the ambiguous parts of the manuscript to enhance clarity. Specifically, we have updated $\\mathbf{B}=\\{b_1,b_2,\\ldots,b_K\\}\\in{ \\mathbb {R}}^{N \\times K}$ to $\\mathbf{B}=\\{b_1,b_2,\\ldots,b_K\\}\\in{ \\mathbb {R}}^{K \\times d}$ for better comprehension.", "claims_and_evidence": "The authors claims there exists the data imbalance of distribution between different view streams, i.e. concept drift problem. Figure 1(a) needs more detailed explanation about the model and evaluation settings. Also, it’s better to explain how this concept drift problem affect the performance in theory rather than just the experimental results.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "Eq. 7 needs further explanation about the historical prototype self-similarity term in the denominator. It appears that the probability $Q_i^b$ cannot sum up to 1 if this term exists, and the authors do not explain why this term exists.", "experimental_designs_or_analyses": "Yes, the experimental designs and analyses are valid.", "relation_to_broader_scientific_literature": "In summary, the paper makes an important contribution by bridging the gap between static multi-view clustering methods and dynamic streaming clustering methods. Its focus on concept drift, distribution consistency, and the alignment of historical and current knowledge opens up new possibilities for more effective clustering in continuously evolving datasets. These contributions are well-aligned with the broader scientific efforts to improve streaming learning and multi-view clustering.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths\n- The paper is well-constructed and easy to read.\n- The experiments are conducted over 8 datasets and 12 state-of-the-art methods.\n\nWeaknesses \n- The authors should include the notation of Z and H in Figure 2 for better understanding.\n- The authors claim they might be the first work to reveal the issue of concept drift in the context of streaming view clustering, but the paper lacks thorough investigation to address the problem.\n- In section 3.1, P is of the dimension K*d, but in section 3.4, P seems to have the same dimension as B, N*K. Also, it would be better to clarify the dimensions of the later-introduced variable, such as p, h, b in the context for better understanding.", "other_comments_or_suggestions": "No.", "questions_for_authors": "please refer to the weakness part.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SFWyrKGSwZ", "reviewer": "Reviewer_WHyf", "summary": "This paper identifies the concept drift problem in streaming view clustering, which causes outdated models to fail to adapt to new view data. To address this, the authors employ knowledge aggregation learning to simultaneously reconstruct prototype knowledge from the features and reconstruct features from the prototype knowledge. To mitigate the concept drift between view streams, distribution consistency learning aligns the prototype knowledge of the current view with the historical knowledge, ensuring consistency in the data distributions across the collected views. Finally, knowledge guidance learning is introduced to leverage prototype knowledge to guide data distribution, thereby enhancing the clustering structure of feature representations.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**W1: The cross-attention mechanism appears to have been used in other works as well. Could the authors clarify the novelty of its application in this context?**\n\n**AW1:** Other methods, such as ProImp [1], are designed for static multi-view clustering tasks, where the attention mechanism primarily focuses on one-time data reconstruction and clustering. In contrast, DSVC is designed for streaming multi-view clustering scenarios, where view data arrives dynamically over time and is subject to concept drift. The Knowledge Aggregation Learning (KAL) module continuously processes newly arrived views and interacts dynamically with the historical knowledge repository to maintain global consistency in data distribution. The KAL module in this dynamic environment fundamentally differs from conventional objectives.\n\n**Reference:** \n\n[1] Incomplete multi-view clustering via prototype-based imputation, IJCAI, 2023.\n\n**W2:The distributed consistency loss used in the first view of this paper lacks a correlation ablation experiment.**\n\n**AW2:** We have added ablation experiments for the Distribution Consistency Loss (DCL). As shown in the table, incorporating DCL in the first view significantly enhances clustering performance.\n\n| Datasets       | ALOI-10 | HandWritten | Landuse-21 | Scene-15 | stl10-fea | ALOI-100 | YoutubeFace-sel-fea | ALOI-1000 |\n|----------------|---------|-------------|------------|----------|-----------|----------|---------------------|-----------|\n| **DCL loss**   | acc     | acc         | acc        | acc      | acc       | acc      | acc                 | acc       |\n| ✗              | 79.89   | 88.05       | 26.52      | 42.91    | 44.71     | 78.55    | 21.50               | 50.13     |\n| ✓              | **86.69** | **95.44**  | **28.52**  | **45.33**| **63.41** | **80.53**| **25.09**           | **58.32** |\n\n---\n\n**W3: In Section 3.5, the explanation of why the clustering structure can be enhanced is not very clear.**\n\n**AW3:** The knowledge guidance learning loss computes the similarity relationships between prototypes and samples, firstly.  Then, it maximizes the similarity between features and their corresponding prototypes while minimizing the similarity between features and prototypes of different classes, thereby reinforcing the intra-cluster feature consistency and the inter-cluster feature discriminability.\n\n**W4: The explanation for why DSVC can effectively handle large-scale datasets is not comprehensive.**\n\n**AW4:** Our DSVC leverages a prototype knowledge base to transfer historical knowledge, which considers only the number of prototypes $K$ and current-view samples $n$ during computation. Thus, the computational complexity of $O(K \\times n)$, where $(K \\ll n)$.   Moreover, the memory overhead during updates is significantly reduced to $O(K \\times d)$, where $d$ denotes the embedding dimension. Therefore, our method can effectively process large-scale data under limited conditions.\n\n**W5: Would the quality of the first view negatively impact the training of subsequent views?**\n\n**AW5:** As shown in Fig. 4 and 9, the quality of the first view does not have a significant negative impact on subsequent training. For example, in the HandWritten dataset, despite substantial variations in the quality of the first view, the final results remain relatively stable as more view data is continuously collected. Even when the initial view quality is suboptimal, the overall performance difference remains minimal.\n\n**W6: In Fig. 3 and 4, it can be observed that there is a sudden performance improvement after training on a particular view.**\n\n**AW6:** This phenomenon occurs because different views are collected from distinct sources, resulting in significant variations in feature quality across views. Consequently, during training, when a view with highly distinguishable feature representations (i.e., a view with high inter-class separability) is encountered, the performance experiences a noticeable boost.\n\n**W7: In Table 2 and Fig. 11, it is observed that the performance on the Stl10-fea dataset exhibits significant fluctuations.**\n\n**AW7:** \n\n**Large standard deviation:** This is primarily due to the significant feature quality disparity between different views (feature dimensions are 512, 1024, and 2048, respectively) of the Stl10-fea dataset, along with the large-scale sample but limited number of views. As a result, it becomes challenging to fully exploit the complementary information among views during training, which leads to considerable performance fluctuations for all methods.\n\n**Performance fluctuations:** Our method is sensitive to the parameters $\\alpha $ and $\\beta $. To intuitively investigate the impact of $ \\alpha $ and $ \\beta $ on performance, we conducted the sensitivity analysis presented in Fig. 11. In our approach, we set $ \\alpha $ and $ \\beta $ to 0.001 and 1, respectively, for the STL10-fea dataset.", "claims_and_evidence": "The superior performance of DSVC and its effectiveness in mitigating concept drift are well-supported by extensive experimental evidence, including results across multiple datasets. Improvements in both accuracy and efficiency are clearly documented. However, the theoretical analysis and description of the DSVC mechanism, particularly the explanation of knowledge guidance learning, could be made clearer.", "methods_and_evaluation_criteria": "The proposed deep streaming view clustering method is well-suited to address the streaming view clustering problem in multi-view clustering. The benchmark datasets and performance metrics used are appropriate for evaluating the effectiveness of the method. Through experimental design, DSVC is compared with existing methods, validating its effectiveness.", "theoretical_claims": "The theoretical claims regarding streaming view clustering and the necessity of addressing concept drift between view streams are well-supported. However, the mathematical formulation of DSVC could benefit from greater clarity. Specifically, the definition of how knowledge guidance learning enhances the clustering structure is lacking in terms of formal mathematical work.", "experimental_designs_or_analyses": "The experimental design is well-structured, with clear benchmarks and comparisons to other state-of-the-art (SOTA) methods. The paper analyzes the impact of different view stream sequences to validate the effectiveness of the proposed method in mitigating concept drift.", "relation_to_broader_scientific_literature": "This paper positions itself within the context of streaming view clustering, comparing DSVC with other multi-view clustering methods. It highlights the lack of the research regarding streaming view clustering and proposes a novel approach for addressing the streaming view clustering.", "essential_references_not_discussed": "The paper cites key references; however, it would benefit from a more in-depth discussion on the connection with related techniques for handling concept drift.", "other_strengths_and_weaknesses": "Strengths:\n\n1. Due to knowledge aggregation learning, the obtained feature representations and prototype knowledge are more representative.\n2. This paper employs distribution consistency learning to align the prototype knowledge of the current view with the historical knowledge distribution, effectively mitigating the concept drift issue.\n3. The performance significantly outperforms the comparison methods.\n4. The experiments are diverse and account for various types of data.\n\nWeakness:\n\n1. In this paper, the Knowledge Aggregation Learning module uses cross-attention mechanisms to reconstruct prototypes and features. This approach appears to have been used in other works as well. Could the authors clarify the novelty of its application in this context?\n2. In Section 3.4, this paper proposes using the distribution consistency learning loss to ensure that the reconstructed features do not deviate from the original data distribution when processing the first view. However, no related experiments are presented to validate the effectiveness of this strategy. It is recommended to provide relevant experimental results to support this approach.\n3. In Section 3.5, the explanation of why the clustering structure can be enhanced is not very clear.\n4. In the last paragraph of Section 4.4, the explanation for why the method can effectively handle large-scale datasets is not comprehensive.\n5. In this paper, the training of subsequent views appears to depend on the previously trained views. Would the quality of the first view negatively impact the training of subsequent views if the first view is of poor quality?\n6. In Fig. 3 and 4, it can be observed that in certain datasets, there is a sudden performance improvement after training on a particular view. Please explain the relevant reasons.\n7. In Table 2 and Fig. 11, it is observed that the performance on the Stl10-fea dataset exhibits significant fluctuations. Is this behavior due to the inherent characteristics of the dataset, or is there another underlying reason?", "other_comments_or_suggestions": "The methodology explanation and experimental analysis in this paper require a more detailed and comprehensive discussion from the authors.", "questions_for_authors": "For related issues in this section, please refer to the Weakness.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xpO8f3npYa", "reviewer": "Reviewer_R49s", "summary": "This paper proposes a deep streaming view clustering algorithm (DSVC). It considers the scenario where data is acquired in the form of view streams in clustering tasks. DSVC aligns the prototype knowledge of the current view with the historical knowledge distribution, thereby mitigating the concept drift issue between streaming views. Furthermore, the aligned prototype knowledge guides the current data distribution, which enhances the clustering structure. Experimental results demonstrate that DSVC outperforms state-of-the-art methods.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for taking the time to review our submission and provide valuable feedback. \n\n**W1: Ablation study without the cross-attention mechanism**\n\n**AW1:** To validate the effectiveness of the cross-attention mechanism, we conducted an ablation study. As shown in the following table, incorporating the cross-attention mechanism in our work can significantly enhance performance. This is because the cross-attention mechanism enables the prototype knowledge to better fit the data distribution while enhancing the intra-cluster consistency and inter-cluster discriminability of the features. The resulting prototype knowledge and features work in concert with our approach to achieve optimal performance.\n\n### Ablation experiments on eight datasets (with or without cross-attention mechanism)\n\n| Datasets             | ALOI-10 | HandWritten | Landuse-21 | Scene-15 | stl10-fea | ALOI-100 | YoutubeFace-sel-fea | ALOI-1000 |\n|----------------------|---------|-------------|------------|----------|-----------|----------|---------------------|-----------|\n| **Cross-attention mechanism**  | acc     | acc         | acc        | acc      | acc       | acc      | acc                 | acc       |\n| ✗                    | 74.22   | 86.25       | 20.33      | 31.13    | 47.64     | 60.92    | 19.42               | 36.20     |\n| ✓                    | **86.69** | **95.44**  | **28.52**  | **45.33**| **63.41** | **80.53**| **25.09**           | **58.32** |\n\n---\n \n**W2: In Section 3.5 of the manuscript, the description of how the Knowledge Guidance Learning (KGL) module enhances the clustering structure need to be clarified more clearly.**\n\n**AW2:** Our Knowledge Guidance module encourages samples to converge toward their corresponding prototypes while pushing away unrelated samples. This ensures that samples within the same cluster exhibit higher similarity, while those from different clusters exhibit lower similarity(i.e., intra-cluster compactness and inter-cluster separability.).\n\n\n**W3: Is there any method to reduce the discrepancies in the training results of the initial view?**\n\n**AW3:** Your question is highly insightful. In the experiments shown in Fig. 4, the first-view data in different streaming sequences originates from different sources, resulting in significant variations in data quality. Consequently, the training outcomes for the first view exhibit corresponding discrepancies. How to effectively exploit the information from the first-view data and enhance clustering performance in the absence of guidance information remains an open challenge. We will further investigate this issue in our future work.\n\n**Q1: What advantages does the proposed distribution consistency learning have over conventional contrastive learning strategies?**\n\n**AQ1:**\nDifference: Traditional contrastive learning aims to learn discriminative feature representations by pulling positive sample pairs closer while pushing negative pairs apart. Its primary objective is to enhance feature discriminability. In contrast, our Distribution Consistency Learning (DCL) measures the divergence between the probability distributions of two samples, with the goal of directly minimizing the discrepancy between distributions.\nAdvantage: Traditional contrastive learning relies heavily on the construction of positive and negative sample pairs, where the quality and quantity of these pairs directly impact model performance. For instance, when there are noisy or low-quality samples (i.e., hard samples), the model can overly focus on these hard samples, which leads to overfitting and a lack of robustness. In contrast, our Distribution Consistency Learning (DCL) only requires measuring the similarity between sample distributions, eliminating the need for explicit positive and negative pair construction. This avoids performance degradation caused by low-quality negative samples, thereby enhancing the robustness of the model.\n\n**Q2: The standard deviation of Stl10-fea data set is relatively large. Is this phenomenon normal?**\n\n**AQ2:** As shown in Tables 1 and 2, the clustering performance on the Stl10-fea dataset exhibits a relatively large standard deviation across almost all methods. This is primarily due to the significant feature quality disparity between different views (it can be seen in Fig. 4), along with the large-scale sample but limited number of views. As a result, it becomes challenging to fully exploit the complementary information among views during training, leading to considerable performance fluctuations across different random initializations for all methods.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes. The metrics,  including ACC, NMI and ARI, are commonly used in clustering analysis.", "theoretical_claims": "N/A. There is no theoretical claim.", "experimental_designs_or_analyses": "Yes. I have checked the comparison to existing methods, ablation study and visualization analysis. They well support the claims.", "relation_to_broader_scientific_literature": "Existing multi-view methods assume all data views are collected in advance. However, in practical scenarios, new data views are collected over time. This paper proposes a DSVC method to solve this. This is meaningful and would have a broad impact in literature.", "essential_references_not_discussed": "[1] is also a recently proposed work to explore the clustering task when data views are collected overtime. This could be introduced and discussed. \n\n\n[1] Wan et al. Fast Continual Multi-View Clustering With Incomplete Views. TNNLS 2024.", "other_strengths_and_weaknesses": "Strengths:\n\n1. This paper identifies and mitigates the issue of concept drift in streaming view clustering, which is novel in literature.\n\n2. The proposed method achieves completing results in experiments.\n\n3. The paper is well organized and easy to follow. \n\nWeaknesses:\n\n1. The authors mention the use of a cross-attention mechanism to reconstruct prototypes and features in Section 3.3, but there is no ablation study provided in the experiments to assess the impact of this component.\n\n2. In Section 3.5 of the manuscript, the description of how the Knowledge Guidance Learning (KGL) module enhances the clustering structure need to be clarified more clearly.\n\n3. As shown in Fig. 4, the training results of the first view exhibit significant differences depending on the view streams of the different sequences. Is there any method to reduce the discrepancies in the training results of the initial view?", "other_comments_or_suggestions": "Please see Section **Other Strengths And Weaknesses**", "questions_for_authors": "1.\tWhat advantages does the proposed distribution consistency learning have over conventional contrastive learning strategies? \n2.\tBased on Tables 1 and 2, I noticed a relatively large standard deviation for the Stl10-fea dataset. Is this phenomenon expected or normal?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: All Reviewers agree on the Acceptance of this paper.
Decision: Accept (poster)