Relationship Alignment for View-aware Multi-view Clustering
Authors: Shuangmei Peng
Zhe Chen
Tianyang Xu
Xiaojun Wu
Venue: ICLR 2026 Poster (2026)
Keywords: Relationship Alignment; View-Aware Contrastive Learning; Multi-View Clustering
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Multi-view clustering improves clustering performance by integrating complementary information from multiple views. However, existing methods often suffer from two limitations: i) the neglect of preserving sample neighborhood structures, which weakens the consistency of inter-sample relationships across views; and ii) inability to adaptively utilize inter-view similarity, resulting in representation conflicts and semantic degradation. To address these issues, we propose a novel framework named Relationship Alignment for View-aware Multi-view Clustering (RAV). Our approach first constructs view-specific sample relationship matrices from deep features and aligns them with the global relationship matrix to enhance cross-view neighborhood consistency and facilitate accurate measurement of inter-view similarity. Simultaneously, we introduce a view-aware adaptive weighting mechanism for label contrastive learning that dynamically adjusts the contrastive intensity between view pairs based on deep-feature similarity: higher-similarity views lead to stronger label alignment, while lower-similarity views reduce the weighting to prevent enforcing agreement. This strategy promotes cluster-level semantic consistency while preserving natural inter-view relationships. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches on multiple benchmark datasets. Project website: https://github.com/chenzhe207/RAV.

Published as a conference paper at ICLR 2026
RELATIONSHIP ALIGNMENT FOR VIEW-AWARE MULTI- VIEW CLUSTERING
Shuangmei ?,ng Zhe Chen !:2 Tianyang Xu², Xiao-Jun Wu?
1 Anhui University of Technology, Ma'aMnn anshan, 243032, Anhui, China
2 Anhui Province Key Laboratory of Digital Twin Technology Metallurgical Industry, Anhui, China
3 Jiangnan University, Wuxi, 214122, Jiangsu, China
(psm, c2 ) @auut. edu. en, (t (tianyang, xu, wu wu_xiaa @ edu. cn
ABSTRACT
Multi-view clustering improves clustering performance by integrating complemen- tary information from multiple views. However, existing methods often suffer from two limitations: i) the neglect of preserving sample neighborhood structures, which weakens the consistency of inter-sampl relationships across views; and ii) inability adaptively utilize inter-view similarity, resulting in representation conflicts and semantic degradation. To address these issues, we propose novel framework named Relationship Alignment for View-aware Multi-view Clustering (RAV). Our approach first constructs view-specific sample relationship matrices from deep features and aligns them with the global relationship matrix to enhance cross-view neighborhood consistency and facilitate accurate measurement of inter-view similar- ity. Simultaneously, we introduce a view-aware adaptive weighting mechanism for label contrastive learning that dynamically adjusts the contrastive intensity between view pairs based on deep-feature similarity: higher-similarity views lead to stronger label alignment, while lower-similarity views reduce the weighting to prevent forcing agreement. This strategy promotes cluster-level semantic consistency while preserving natural inter-view relationships. Extensive experiments demonstrate that our method consistently outperforms -tate-of-the--e aht approaches on multiple benchmark datasets. Project website: https:/ggthub.com/chen//// com/chennzhee227//RA
INTRODUCTION
With the rapid growth of multi-modal data and big data, multi-view clustering (MVC) Chen et al. (2023b); Dong et al. (2023); Eisenberg et al. (2025); Trosten et al. (2023) Wan et al. (0044) Deng et al.
(2024) has gained widespread attention in recent years. Compared to traditional single-vies clustering,
MVC can effectively integrate complementary information from different sources, capturing richer
intrinsic data structures and leading to more accurate sample partitioning Liu et al.] (202)) [Wen et al.)
(2026). Consequently, MVC has been widely applied in numerous fields, including computer vision Xie et al. (020)), natural language processing Ke et al. (2024); Nadkarni et al. I). and social network analysis Fang et al. (2023b); Banez al. (2022). Depending on the use of deep learning techniques, existing MVC methods can be broadly categorized into two types: traditional MVC methods and deep MVC methods.
The significant advancements in deep learning for representation learning have driven deep MVC Wen al. 2023);; Trosten et al. (202T) | Xiioo et al. (2025) to gradually become mainstream. Compared to traditional MVC, deep MVC leverages the powerful representation learning capabilities of deep neural networks and has demonstrated promising clustering performance. For instance, [in et al. (202I) learns view-specific representations through intra-view reconstruction loss while maintaining cross- view consistency using mutual information- based contrastive learning. Xu et al. (2022) proposes a multi-level feature learning framework that alleviates the confliet between consistent representation learning and view-specific feature reconstruction. Yan et al. (2023) further employs the inter-sample structural relationships to guide contrastive learning, thereby achieving consistent representation
"Corresponding author.

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Published as a conference paper at ICLR 2026
Cross-Vies Relation Alignment
View
D
Deep feature
z
g
Sample relation
z
x
S
Caansdiyy Birzet
z
D
z
S
View-Specific Autoencoder
2
Q¹
D
View-aware
Semantic label
X"
Semantic Inbel
Z
2
Q"
View v
DE
Deep feature
V Viee-aware Label Contrastive Learning
Figure 1: An illustration of the proposed RAV framework. The model crucially incorporates two modules: cross-view relation alignment to maintain neighborhood structures, and view-aware adaptive weighting in label contrastive learning to counteract representation degradation from view dissimilarity, thereby achieving robust multi-view clustering.
learning. These approaches effectively capture complex nonlinear relationships across views, thereby significantly improving both feature representation quality and clustering efficiency.
Meanwhile, contrastive learning Chen et al. (2025c); Dong et al. (025);; Chen et al. (2024 2025b) can effectively capture high-level semantics while suppressing irrelevant information. Current contrastive learning- deep MVC approaches primarily focus on two levels: sample-level and cluster-level. At the sample level, feature consistency is enhanced by bringing similar samples closer together and pushing dissimilar samples further apart. At the clustering level, the focus is on aligning the clustering distributions across views to achieve consistency in clustering semantics. For example, [i et al. (202T) introduces both sample-level and cluster-level contrastive objectives to jointly optimize feature representations and clustering assignments. Chen al. (2023a) proposes cross-view cluster assignment contrastive learning to achieve consistent cluster distributions. Cui et al.) (2024) develops a dual contrastive mechanism that systematically constructs positive and negative pairs for learning consistent representations. These research contributions provide novel insights for multi-view clustering tasks, while establishing solid foundation for subsequent contrastive learning-based deep MVC methodologies.
Despite considerable advances in MVC, several challenges remain. Many methods fail preserve the sample neighborhood structure between views, compromising the consistency of cross-view sample relationships. Moreover, most contrastive learning- baased methods treat all views equally and force their alignment, which can disrupt semantic structures and cause representation conflicts when view differences are substantial. Although recent works Xu et al. (2023): Wu al. (2024) attempt to mitigate representation degradation by introducing adaptive weighting at the feature level, some shortcomings remain. These methods primarily focus on introducing weight adjustment at the feature level. However, they fail to adequately consider the consistency of cross-view sample relational structures and the semantic consistency at the cluster level. Consequently, these approaches may still force low-similarity views to undergo consistency learning, leading to semantic conflicts and disruption of natural inter-view relationships. To overcome these issues, we propose novel framework that combines sample relationship alignment with view-aware adaptive label contrastive learning. Our method first constructs a sample relationship matrix from deep features for each view and aligns it with the global relationship matrix. This effectively enhances cross-view neighborhood
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consistency while preserving the local neighborhood structure of the samples. Meanwhile, cluster assignment matrices are generated via a shared MLP. To further enhance semantic consistency in clustering. We introduce view-aware adaptive weighting strategy to adjust the contrastive strength based on the deep feature similarity between view pairs. Highly similar view pairs are assigned greater weight enhance semantic consistency, while view pairs with low similarity are given smaller weight to avoid representation conflicts caused by forced alignment. This strategy effectively reduces representation degradation caused by view discrepancies and maintains the natural relationships between views.
The main contributions of this paper are summarized follows:
We introduce global-guide- guide-local sample relation alignment module that preserves neigh- borhood structures and enhances cross-view sample relationships consistency by aligning view-specific relation matrices with global relation matrix.
We propose a view-aware adaptive weighting mechanism for label contrastive learning. which dynamically adjusts the alignment strength based on the similarity between views, effectively mitigating semantic conflicts.
Extensive experiments multiple benchmarks show that our method achieves state-of- the-art performance across standard clustering metrics, demonstrating its effectiveness and generalizability.
RELATED WORK
2.1 DEEP BULTI-VIEW CLUSTERING
The advancement of deep learning has greatly propelled the development deep MVC. Modern deep MVC methods capitalize neural networks' strong nonlinear representation capabilities to effectively learn consistent, discriminative shared latent representations from multi-view data. These approaches be broadly categorized into several lines of research. Graph-based methods focus on leveraging inter-sample relationships for representation learning and clustering. For instance, Fei al. 2025 proposes a graph structure-aware contrastive clustering model that combines graph topology with node attribute features. Similarly. Wang et al. (2025) improves the quality and robustness of consensus graph learning through sample-level fusion and clustering effectiveness guidance. Subspace-base methods focus on deriving consistent representations across views. For example, Yu et al.) (2025) introduces a pseudo-label-guice clustering framework that maintains structural coherence in sample affinity matrices. [Lin et al. (2025) proposes a low-rank multi-view subspace clustering based on a Transformer autoencoder to learn consistent and discriminative shared subspace representation. Reconstruction-bee based methods typically utilize autoencoders to learn a shared latent space and can reconstruct the original data or its representation. Yan et al. (2025), for instance, applies self-supervised strategy to learn consistent view representations by reconstructing view features, achieving discriminative clustering under the constraint of view consistency.
bidirectional discriminative subspace
2.2 CONTRASTIVE LEARNING
Contrastive learning, as a powerful unsupervised learning paradigm, demonstrates unique advantages in handling data heterogeneity and cross-view alignment tasks. It learns highly discriminative and consistent representations by constructing positive and negative sample pairs. Tang et al. (2020) proposes a decoupled contrastive MVC approach based on higher-order graph walks, achivvng reliable clustering representations through intra-view and inter-view contrastive learning. Cui et al. (2024) designs dual contrastive loss combining dynamic clustering diffusion and neighborhood- guided alignment, enhancing intra-cluster compactness while improving inter-cluster separation. Dong et al. (2025) further presents a view-graph-basee progressive fusion method, achieving rep- resentation consistency through dual contrastive learning within and across views. Yi et al. (2025) attempts to leverage shared information to construct anchor points for contrastive learning, mitigating view-speciffcity and noise interference.
Despite existing methods having made significant progress, most remain limited to aligning features clustering distributions, generally overlooking the construction of consistent sample relational
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structures and failing to adequately address representation degradation caused by view discrepancies. In contrast, our approach maintains cross-view neighborhood consistency by aligning sample rela- tional structures and adaptively leverages inter-view similarity guide label contrastive learning. This effectively enhances semantic consistency without distorting natural view relationships.
3 METHOD
3.1 PRELIMINARIES
This section introduces our multi-view clustering framework, as depicted in Figure The architecture consists of three core components: View-Specific A Autoencoder Modules, a Cross- View Relation Alignment Module, and a View-aware Vieweaware Label Contrastive Learning Module. Given multi-view dataset with V views denoted as X represented as X x2; x R xd. with being the number of samples and dy the feature dimension of view the framework is designed to jointly optimize these modules. This integrated approach effectively handles disparities in view similarity while promoting consistency in cross-view representations.
[X X X" where the data from the v-th view is
3.2
VIEW-SPECIRIC AUTOENCODER
In multi-view clustering, the quality of feature representations is critical to clustering performance. To extract robust latent features from raw multi-view data, which often contain noise and redundancy. we employ view-specific autoencoders for each view. Formally, for the w-th view, we define an encoder f and a decoder g" The latent representation of the i-th sample in view v obtained as:
f (x",;; 0*),
where 0 denotes the learnable parameters of the encoder for the w-th view, and d is the dimensionality of the resulting latent feature. The latent representation zạ is then passed to the corresponding decoder g" to reconstruct the original input, formulated
n (2",5 Φ²) g" (x" 0²); o²))
where S denotes the reconstruction of the i-th sample from the v-th view, and ø" represents the trainable parameters of its decoder. The overall reconstruction loss for training all autoencoders defined as:
CREC
12
3.3 CROSS-VIEW RELATIONSHIP ALIGNMENT
To enhance the consistency of sample relationships across views while preserving local neighborhood structures, we introduce cross-view relation alignment module. First, deep features Z" for each view are obtained using the view-specific encoders. The pairwise similarity between samples within view is then computed via a Gaussian kernel, thereby constructing intra-view structural relationships. Specifically, the similarity between the i-th and k-th samples in the w-th view is given by:
|z
x 2
S = exp
where si denotes the similarity between the i-th and k-tth samples in the w-th view. smaller feature distance corresponds to higher similarity. Conversely, the larger the feature distance, the lower the similarity. Based on equation (4). we compute the pairwise similarities for each view and store them in view-specific relation matrix {S" = sy; : N _
Je=1 To construct global relation matrix that integrates information from all views, we first concatenate the deep features as follows:
Z Concat(Z' 1 Z ".,,..,,ZZ" 2z),
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where Z (VVd) represents the concatenated global features. The global relation matrix S S2;:s....:;; E R N then computed using the same similarity measure defined in equation (4). We align each view-specific relation matrix with this global matrix via ctobal-supervisellose contrastive learning objective, which pulls positive pairs closer while pushing negative pairs apart. The resulting cross-view relation alignment loss is formulated as:
N
e (t.)
Cs
log
(6)
N
2k=1 e Ms ST./TF e1/TF"'
where 7F temperature hyperparameter, and d(s), Sk.)) denotes the cosine similarity function, defined as:
(s ( Sk,))
d(s Sk.))
|| sk.ll|
The alignment loss improves cross-view relational consistency by preserving sample neighborhood structures, ensuring that neighboring samples remain close while distant ones are separated. Con- sequently, it enhances both feature discriminability within views and semantic consistency across views.
3.4 VIEW-AWARE LABEL CONTRASTIVE LEARNING
After obtaining the view-specific representations, we project the features of each view through shared MLP to generate the cluster assignment matrices {Q" E V 10-1- Each entry 9; denotes the probability that the i-th sample is assigned to the j-th cluster the v-th view, obtained by applying the Softmax function along the cluster dimension. To promote clustering consistency across views, we apply contrastive learning at the qusstrr-asignmentt level. For the j-th cluster assignment vector q, (i.c., the j-th column of Q"), consider all possible assignment pairs across views and clusters, totaling (V KK- 1) pairs. Among these, pairs originating from the same cluster index j but different views (u # v) are treated as positive pairs, amounting to (V- - positives. The remaining V(K- - 1) pairs considered negatives. The contrastive loss between view and view is defined as:
K
e dia 9"
p(
log
(8)
K
Σ Em=u,u e 9 9..// -el/Ti"
k=
where TL is the temperature parameter of label contrastive learning. Afterward, the total label contrastive loss is given by:
V K ry log r"
Co 2E ((on))
w=1j=1
where rj= = acts regularization term that prevents all samples from being assigned single cluster, thereby avoiding trivial solutions.
E Cy The first term enhances cross-view clustering consistency, while the second
However, this approach does not account for inherent feature distribution discrepancies across views. When contrastive learning forcibly aligns view pairs with substantial differences, it may distort genuine semantic structures and cause representation degradation. To address this issue, we propose view-aware adaptive weighting strategy for label contrastive learning, which dynamically modulates influence each view pair based on their deep feature similarity. Specifically, we first employ the Wasserstein Distance (WD) Shen et al. (2018) to quantify the discrepancy between the feature distributions of two views. The WD between view v and view u is defined as:
N
WD N2
(10)
1=1
where z", and z denote the deep features of the i-th sample in view v and the k-th sample in view u, respectively. The adaptive weight between views and is then calculated based on their
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Wasserstein Distance as follows:
(2")
(v.u)
(1I)
W WD)
where ((n.a) denotes the adaptive weight between the representations Z" and Z". The pairwise weights of all views form V x V matrix W. This matrix enables dynamic weighting strategy: view pairs with high feature similarity (i.e., small WD values) are assigned larger weights to strengthen their contribution during contrastive learning, while pairs with large representation discrepancies (i.e., high WD values) are assigned smaller weights to reduce potential negative effects. Integrating this weighting mechanism into the label contrastive learning objective, we obtain the final view-aware adaptive weighting loss:
Lo
(((,u)) + ((.n)) (((,))) +
rj log
(12)
V=1 uaf
==
3.5
THE OVERALL LOss FUNCTION
Based on the foregoing formulation, the overall objective function integrates the three key components as follows:
Lotal = CREC + Ai + A2Ls,
(13)
where \ and A2 are balancing coefficients, LREC denotes the reconstruction loss, Lo represents the view-aware adaptive label contrastive loss, and Ls corresponds to the cross-view relation alignment loss.
Once the model converges, the clustering labels can be obtained as follows:
yj arg max
9:5
(14)
The full process of our RAV is summarized in Algorithm 1.
Algorithm : The optimization of RAV.
Input:
Multi-view dataset [X"]V 1 The number of samples N; The number of max epochs
is T; The number of clusters is K: The parameters Å1, X2-
2: Initialization: Initialize autoencoder parameters by minimizing LREC in Eq. (3)
3: (using mini-batch training)
4: for = to T do
5: Obtain the relationship matrix S" for the specific view and the global relationship matrix by
Eq.
6: Obtain the weight matrix W by Eq. [T]..
7: Optimize (0".0") " by minimizing Lintal in Eq. 13
8: end for
9: Calculate the predicted labels by Eq. (14).
Output: Y [911 ....... yN]. .UN].
4 EXPERIMENT
4.1 DATASETS AND EXPERIMENTAL SETTING
Datasets, We evaluate our model on ten benchmark datasets. Table summarizes their key character- istics, including sample size, number of views, number of clusters, and feature dimensions for each view. NGs Yan et al. (2025): This dataset consists 500 documents, which have been preprocessed using three different methods obtain three distinct views. Digit-Product Cui al. (2024) This

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dataset is derived from MNIST and Fashion Handwritten digits, containing 30,000 samples and two views. ALOICCui et al. (2024] This dataset contains 10,800 samples and 10 clusters, with four views extracted from each image, representing color similarity, Haralick, HSV, and RGB features. Cora Fang et al. 2023a):: This dataset contains 2,708 documents, with four features selected as the four views: content, inbound, outhound, and citations. It is categorized into seven clusters. NUSWIDE Chua et al. (2009): This dataset consists of 5,000 images, classified into categories. Caltech-5V
Xu al. (2022): This dataset is an RGB image dataset containing 1,400 images, covering WM, CENTRIST, LBP, GIST, and HOG features. NoisyMNIST Wang et al. p: This dataset comprises 50,000 samples, organized into 10 clusters. Youtube Video Madani al. (2012): This dataset consists of 101, ,499 samples, divided into 31 classes. 3Source This dataset contains 169 samples, 3 views, and 6 classes. Fashion Xiao et al. (2077): This dataset contains 10,000 images, 3 views, and 10 clusters.
Table 1: Description of the used multi-view datasets.
Dataset
Samples
Views
Clusters
Dimensionality
NGs
500
3
2000/2000/2000
Digit-Produce
30,000
2
10
1024/1024
ALOI
10,800
4
100
77/13/64/125
Cora
2,708
4
7
2708/14333 433/27/0/////7
NUSWIDE
5,000
5
5
65/226/1 45/74/129
Caltech-5V
1,400
5
40/254/928/51//
NoisyMNIST
50,000
2
10
784/784
Youtube
101,499
3
31
512/647/838
3Sources
169
3
6
1560/3631/3065
Fashion 10,000
3
784/784/784
Implementation Details. All experiments are implemented in PyTorch 1.12.1 on an NVIDIA RTX 4090 D GPU. The model is optimized using Adam with a fixed learning rate of 0.0003 and batch size 256. Both pretraining and fine-tuning phases are set to 200 epochs. We introduce two hyperparameters Å₁ and '2 with ranges of [0.00001, 0.0001, 1000] and [0.00001, 0.0001. respectively. The Gaussian kernel bandwidth a is set to 1.0, and both TF and Ty are fixed to 0.5. All baseline methods conducted in the same experimental setup, using either the parameters recommended in the baseline methods or those optimized through parameter search for the best performance.
4.2 COMPARED METHODS AND RESULTS
We compare nine representative multi-view clustering methods across ten benchmark datasets to evaluate our approach. MFLVC Xu al.) (2022): This method is primarily used for multi-level feature learning in multi-view clustering. GCFAgg Yan et al. (2023): This method mainly utilizes sample similarity structures to guide contrastive learning. SEM Xu et al. (2023): This method mostly guides contrastive learning by adjusting view weights. MVCAN Xu et al. (2024): This method alleviates the negative impact of noisy views and optimizes the learning of individual image representations. SCMVC et al. (2024]: This method adaptively strengthens useful views in feature fusion while weakening unreliable views. DDMVC Xu et al. (2025): This method considers diversity and discriminative feature learning. SSLNMVC Yan et al. (0025): This method introduces the UProjection module, which enhances the expressiveness of consistent features by feature resampling and concatenating the fused features before and after resampling. AICN-MLM [Shu et al. (2025): This method proposes fuzzy anste lulti-level matching contrastive network for multi-view document clustering. DFL-NET Chen et al.) (2025a): This method utilizes orthogonal constraints to separate shared and unique features, then ensures consistency in cross-view clustering through cross-view label comparisons.
We evaluate our method using three widely recognized clustering evaluation metrics: Accuracy (ACC), Normalized Mutual Information (NMI), and Purity (PUR), as shown in Tables and Based on these results, can draw the following conclusions:
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Table 2: Clustering results of all methods on the NGs, Digit-Product, and ALOI datasets.
Datasets
NGs
Digit-Product
ALOI
Evaluation Metries
ACC
NMI
PUR
ACC
NMI
PUR
ACC
NMI
PUR
MFLVC (22 CVPR)
0,932
0.825
0.932
0.991
0.976
0.991
0.435
0.786
0,435
GCFAgg (23 CVPR)
0.894
0.742
0.894
0.988
0.968
0.988
0.790
0.917
0.809
SEM (24 NeurIPS)
0.856
0.673
0.856
0.991
0,976
0.991
0.771
0.899
0.787
MVCAN (24 CVPR)
0,470
0.271
0.470
0.989
0.967
0.989
0.849
0.929
0.864
SCMVC (24 TMM)
0.826
0.638
0.826
0.995
0,986
0.995
0.818
0.925
0.835
DDMVC (25 PR)
0.968
0.931
0.968
0.796
0.907
0.813
SSLNMVC (25 TMM)
0.936
0.842
0.936
0.990
0.973
0.990
0.541
0.814
0.558
AICN-MLM (25 AAAD)
0.912
0.774
0.912
0.991
0,976
0.991
0.788
0.906
0.800
DFL-NET (25 TKDE)
0,904
0.786
0,904
0.998
0.993
0.998
0.825
0.915
0.832
ours
0.980
0.934
0.980
0.998
0.993
0.998
0.826
0.912
0.830
Table 3: Clustering results of all methods on the Cora, NUSWIDE and Caltech-5V datasets.
Datasets
Cora
NUSWIDE
Caltech-5V
Evaluation metrics
ACC
NMI
PUR
ACC
NMI
PUR
ACC
NMI
PUR
MFLVC (22 CVPR)
0.268
0.111
0.377
0.624
0.338
0.624
0.867
0.781
0.867
GCFAgg (23 CVPR)
0,220
0.051
0.304
0.596
0.336
0.596
0.799
0.697
0.799
SEM (24 NeurlPS)
0.220
0.028
0.313
0.588
0.317
0.588
0.901
0.834
0.901
MVCAN (24 CVPR)
0.567
0.385
0.640
0.572
0.290
0.572
0.919
0.856
0.919
SCMVC (24 TMM)
0.311
0.160
0.388
0.622
0.354
0.622
0.871
0.783
0.871
DDMVC (25 PR)
0.323
0.141
0.407
0.607
0.312
0.636
0.771
0.695
0.779
SSLNMVC (25 TMM)
0.277
0.102
0.364
0.637
0.367
0.637
0.881
0.789
0,881
AICN-MLM (25 AAAI)
0.331
0.171
0.418
0.612
0.337
0.612
0.898
0.828
0.898
DFL-NET (25 TKDE)
0.359
0.185
0.435
0.614
0.325
0.614
0.833
0.767
0.833
ours
0.592
0.404
0.598
0.647
0.371
0.647
0.901
0.839
0.901
Table 4: Clustering results of all methods on the NoisyMNIST, Youtube 3Sources, and Fashion datasets.
Dalasets
NoisyMNIST
YoutabeVilen
3Sources
Fashion
Evaluation metrics
ACC
NMI
PUR
ACC
NMI
PUR
ACC
NMI
PUR
ACC
NM1
PUR
MFLVC (22 CVPR)
0.988
0.965
0.988
0.238
0.224
0.324
0.521
0.477
0.669
0.994
0.985
0.994
GCFAgg (23 CVPR)
0.781
0.847
0.836
0.275
0.263
0.361
0.521
0.429
0.6155
0.990
0.974
0.990
SEM (24 NeurIPS)
0.995
0.984
0.995
0.318
0.309
0.404
0.533
0.584
0.716
0.994
0.983
0.994
MVCAN (24 CVPR)
0.933
0.861
0.935
0.244
0.244
0,341
0.562
0.478
0.663
0.856
0,840
0.856
SCMVC TMM)
0.996
0.988
0.996
0.291
0.985
0.994
DDMVC PR)
0.957
0.893
0.957
0.456
0.346
0.592
0.931
0.892
0.931
SSLNMVC (25 TMM)
0.995
0.985
0.995
0.235
0.244
0.430
0.521
0.510
0.686
0.994
0.984
0.994
AICN.MI M (25 AAAT)
0.990
0.971
0.990
0.538
0.472
0.675
0.994
0.985
0.994
DEL-NET (25 TKDE)
0.995
0.983
0.995
0.227
0.246
0.6
0.586
0.572
0.728
0.993
0.982
0.993
0.996
0.986
0.996
0.356
0.332
0.775
0.994
0.984
0.994
Our method outperforms baseline approaches on most datasets, confirming its effectiveness. Notably, on the NGs, Youtube and Cora datasets, the ACC metric improves by 4.4%, 7.8%, and 2.5%, respectively. compared the second- best results. These gains stem from the proposed relation alignment and view-aware adaptive label contrastive learning mechanism. The relational alignment module maintains structural consistency between views, providing stable structure for view similarity measurement and contrastive learning. Meanwhile, the view-aware adaptive weighting mechanism effectively mitigates representation conflicts caused by view differences, preventing the forced alignment of low-similarity views and thereby improving clustering accuracy.
On ALOI and Caltech-5V, our method performs slightly below MVCAN. This may be because MV. CAN does not adopt standard contrastive learning, which reduces its sensitivity to view differences. On the Fashion dataset, our results are comparable to MFLVC, ALM, and SSLNMV. This may be attributed to the relatively simple structure of the dataset and the small view differences, thereby
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(c) NoisyMNIST Figure 2: Parameter sensitivity analysis A and A2 on the NUSWIDE, Caltech-5V, NoisyMNIST. and 3Sources datasets.
(a) NUSWIDE
(b) Caltech-5V
(d) 3Sources
reducing the necessity of the adaptive weighting mechanism. On the Digit-Product dataset, RAV performs similarly to DFL-NET, which mitigates the impact of view inconsistency to some extent by decoupling shared representations from view-specific representations. However, RAV outperforms these methods most other more challenging datasets, further validating the effectiveness of the adaptive weighting mechanism.
Compared to the feature-level contrastive weighting-based SEM and SCMVC methods, our approach achieves comparable performance on simpler datasets like NoisyMNIST and Fashion, while signifi- cantly outperforming them features can more accurately capture the intrinsic similarity between views. Therefore, when guiding label-based contrastive learning, it exhibits superior generalization capability and better preserves natural inter-view relationships.
complex datasets. This demonstrates that the WD based on deep
4.3 MODEL ANALYSES
Parameter Sensitivity: To evaluate the impact X and A2 on the model, we con- duct a parameter sensitivity analysis as illustrated in Figure 2 The experiments set λ within the range [10 10 1.004.10.0 1.003, 100000,10,1001---0001010001 1,, 10 10 and X2 within the range The results demonstrate that when these hyperparameters vary within the specified ranges, the clustering performance on all four datasets exhibits only minor fluctuations. This relatively small variation in performance proves that method is highly robust to hyperparameter selection.
9,10 3, 2,0
Convergence: We observe the changes training loss and evaluation metrics (ACC/NMI) across four benchmark datasets to analyze the convergence characteristics of the proposed method. the convergence curves for the Caltech-SV, Digit-Product, NGs, and MOiSYMNIST datasets. The main observations are as follows: First, the loss function decreases rapidly in the initial training phase and then gradually stabilizes until convergence. Second, the clustering evaluation metrics ACC and NMI continuously increase during training and eventually stabilize. Finally, this convergence trend indicates that our loss function effectively regularizes the model and drives parameter optimization. These results not only demonstrate the convergence stability of our method but also validate its effectiveness in improving clustering performance.
(d) NoisyMNIST Figure 3: Convergence analysis of ours on Caltech-5V, Digit-Product, NGs, and NoiSYMNIST datasets, where each subgraph displays the total loss and both evaluation metrics (ACC/NMI) over training epochs.
(a) Caltech-SV
(b) Digit-Product
(c) NGs
Visualization Analysis: To validate our method's ability to obtain user-friendly clustering structure, we visualize the learned global feature Z on the Digit-Produce dataset using the t-SNE Maaten & Hinton (208)) method. As shown in the Figure 4 with increasing iterations of training, the clustering structure of Z becomes increasingly distinct. The distance between different clusters grows larger, while clusters within the same group become increasingly compact.

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Published as a conference paper at ICLR 2026
(a) Epoch=0
(b) Epoch=50
(c) Epoch=100 (d) Epoch=200
Figure 4: Visualization of the global feature Z for cascading on Digit-Produst dataset. The same color indicates features belonging to the same cluster.
Table 5: Ablation studies on different loss components on the Caltech-5V, NUSWIDE, ALOI, and 3Sources datasets.
Compaments
Callech
NUSWIDE
ALOI
3Sources
Cure
Co Es
ACC
PUR
ACC
NMI
PUR
0.899
0.830
0.899
0.644
0.362
0.644
0.780
0.887
0.789
0.562
0.464
0.686
0.424
0.309
0.439
0.298
0.037
0.311
0.264
0.656
0.264
0.396
0.135
0.408
0.901
0.839
0.901
0.647
0.371
0.647
0.826
0.912
0.830
0.574
0.599
0.775
Table 6: Ablation study on the view-aware adaptive weighting mechanism for NGs, Digit-Produs ALOI and Cora datasets.
Datasets
NGs
Digit Product
AL.OI
Cora
Evaluation metries
ACC
NMI
PUR
ACC
NMI
PUR
ACC
NMI
PLR
ACC
NMI
PUR
W
0.966
0.895
0
0,998
0.993
0.998
0,801
0.903
0.807
0.585
0.393
0.585
ours
0.980
0.934
0.980
0.998
0.993
0.998
0.826
0.912
0.830
0.592
0.404
0.598
Ablation Studies:
To systematically evaluate the contribution of each component, conduct comprehensive ablation study. As summarized in Table LREC. Lo. and Ls represent the reconstruc- tion loss, the view-aware adaptive weighting contrastive loss, and the cross-view relation alignment loss, respectively. Experiments on four benchmark datasets-Callll Show that the full model outperforms variants that remove either the relation alignment module or the view-aware weighting mechanism. These results confirm that relation alignment helps capture semantic structures across views, while adaptive weighting enhances robustness by dynamically moderating the influence of view pairs.
NUSWIDE, ALOI, and
We further examine the specific contribution of the view-aware weighting strategy Table 6 On the NGs, ALOI, and Cora datasets, the full model achieves ACC gains of 1.4%, 3.6%, and 0.7%, respectively, compared to the variant without weighting (Ours w/o W). These improvements indicate that the weighting strategy effectively alleviates representation degradation resulting from view discrepancy. On the Digit-Product dataset, however, performance remains unchanged-likeee -lkeey due to its inherently small inter-view differences, which diminish the need for adaptive weighting. This outcome underscores the particular usefulness our weighting mechanism scenarios with pronounced view disparities.
CONCLUSION
This paper presents multi-view clustering framework that integrates sample relation alignment with view-aware adaptive weighting for contrastive learning. The framework employs globally guided relational alignment module to enhance the consistency neighborhood structures across multiple views and introduces a view-aware adaptive weighting mechanism based on WD to mitigate the negative effects of view similarity discrepancies. The stable neighborhood structure provided by the relationship alignment facilitates more accurate measurement of inter-view differences, enabling more reliable semantic alignment through view-aware adaptive weighting contrastive learning. Extensive experiments show that the proposed method effectively mitigates the impact of view disparity and outperforms existing approaches on multiple benchmarks. Future work will focus on theoretically exploring more robust relational structures and universal similarity measures, while practically extending the method to complex scenarios such as incomplete views and noisy data to further improve its capability in handling view discrepancy challenges.
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6 ACKNOWLEDGMENT
This work was supported in part by the National Natural Science Foundation of China (62306006, 62576152, 62332008, 62336004), the Basic Research Program of Jiangsu (BK20250104), the Fundamental Research Funds for the Central Universities (JUSRP202504007).
REFERENCES
Reginald A Banez, Hao Gao, Lixin Li, Chungang Yang. Zhu Han, and H Vincent Poor. Modeling and analysis of opinion dynamics in social networks using multiple-population mean field games. JEEE Transactions on Signal and Information Processing over Networks, 8:301-316, 2022.
Bowei Chen, Sen Xu, Heyang Xu, Xuesheng Bian, Naixuan Guo, Xiufang Xu, Xiaopeng Hua, and Tian Zhou, Structural deep multi-view clustering with integrated abstraction and detail. Neural Networks, 175:106287, 2024.
Jie Chen, Hua Mao, Wai Lok Woo, and Xi Peng. Deep multiview clustering by contrasting cluster assignments. In Proceedings of the IEEE/CVF international conference on computer vision, pp- 16752-16761, 2023a.
Zhe Chen, Xiao-Jun Wu, Tianyang Xu, and Josef Kittler. Fast self self-guided multi-view subspace clustering. IEEE transactions on image processing, 32:6514-6522 2023b.
Zhe Chen, Xiao-Jun Wu, Tianyang Xu, and Josef Kittler. Dft-net: Disentangled feature learning network for multi-view clustering. IEEE Transactions Knowledge and Data Engineering, 2025a.
Zhe Chen, Xiao-Jun Wu, Tianyang Xu, Hui Li, and Josef Kittler. Deep discriminative multi-view clustering. IEEE Transactions on Circuits and Systems for Video Technology. 2025b.
Zhe Chen, Xiao-Jun Wu, Tianyang Xu, Hui Li, and Josef Kittler. Multi-layer multi-level comprehen- sive learning for deep multi-view clustering. Information Fusion, 116: 102785, 2025c.
Tat-Seng Chua, Jinhui Tang. Richang Hong, Haojie Li. Zhiping Luo, and Yantao Zheng. Nus-wide: a real-world web image database from national university of singapore. In Proceedings of the ACM international conference on image and video retrieval, pp. 1-9, 2009.
Jinrong Cui, Yuting Li, Han Huang, and Jie Wen. Dual contrast-driven deep multi-view clustering. IEEE Transactions on Image Processing, 2024.
Shijie Deng, Jie Wen, Chengliang Liu, Ke Yan, Gehui Xu, and Yong Xu. Projective incomplete multi-view clustering. IEEE Transactions Neural Networks and Learning Systems, 35(8):
10539-10551, 2024.
Zhibin Dong, Siwei Wang. Jiaqi Jin, Xinwang Liu, and En Zhu. Cross-view topology based consistent and complementary information for deep multi-view clustering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 19440-19451, 2023.
Zhibin Dong, Meng Liu, Siwei Wang, Ke Liang, Yi Zhang, Suyuan Liu, Jiaqi Jin, Xinwang Liu, and En Zhu. Enhanced then progressive fusion with view graph for multi-view clustering, In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 15518-15527, 2025.
Ran Fisenberg, Jonathan Svirsky, and Ofir Lindenbaum. COPER: Correlation-based permutations for multi-view clustering. The Thirteenth International Conference on Learning Representations, 2025. URL
net/ httt://ooommmmd=/202pepep
Si-Guo Fang, Dong Huang, Xiao-Sha Cai, Chang-Dong Wang, Chaobo He, and Yong Tang. Efficient multi-view clustering via unified and discrete bipartite graph learning. IEEE Transactions on Neural Networks and Learning Systems, 35(8): 1436-1131111 1447, 2023a.
Uno Fang, Man Li, Jianxin Li, Longxiang Gao, Tao Jia, and Yanchun Zhang. A comprehensive survey on multi-view clustering. IEEE Transactions on Knowledge and Data Engineering, 35(12): 12350-12368, 2023b.
11

---

Published as a conference paper at ICLR 2026
Lunke Fei, Junlin He, Qi Zhu, Shuping Zhao, Jie Wen, and Yong Xu. Deep multi-view contrastive clustering via graph structure awareness. IEEE Transactions on Image Processing, 2025.
Junlong Ke, Zichen Wen, Yechenhao Yang, Chenhang Cui, Yazhou Ren, Xiaorong Pu, and Lifang He. Integrating vision-language semantic graphs in multi-view clustering. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, pp. 4273-4281, 2024.
Yunfan Li, Peng Hu, Zitao Liu, Dezhong Peng, Joey Tianyi Zhou, and Xi Peng. Contrastive clustering. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pp. 8547-8555 2021.
Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, and Peng. Completer: Incomplete multi-view clustering via contrastive prediction. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 11174-11183, 2021.
Yuxiu Lin, Hui Liu, Xiao Yu, and Caiming Zhang. Leveraging transformer-based autoencoders for low-rank multi-view subspace clustering. Putfern Recognition, 161:111331, 2025.
Chengliang Liu, Jie Wen, Yong Xu, Bob Zhang, Liqiang Nie, and Min Zhang. Reliable representation learning for incomplete multi-view missing multi-label classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4766):490- 2025.
Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 97ovv):557--2605 2008.
Omid Madani, Manfred Georg, and David A Ross. On using nearly-independent feature families for high precision and confidence. Asian conference on machine learning, pp. 269-284. PMLR, 2012.
Prakash M Nadkarni, Lucila Ohno-Machado, and Wendy W Chapman. Natural language processing: an introduction. Journal of the American Medical Informatics Association, 18(5):544-51, 2011.
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu. Wasserstein distance guided representation learning for domain adaptation. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.
Zhenqiu Shu, Teng Sun, Yunwei Luo, and Zhengtao Yu. Ambiguous instance-aware contrastive network with multi-level matching for multi-view document clustering. In Proceedings of the AAAI Conference Artificial Intelligence, volume 39, pp.
2025.
Chang Tang, Xinwang Liu, Xinzhong Zhu, En Zhu, Zhigang Luo, Lizhe Wang, and Wen Gao. Cgd: Multi-view clustering via cross-view graph diffusion. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pp- 5924-5931, 2020.
Daniel Trosten. Sigurd Lokse, Robert Jenssen, and Michael Kampffmeeer. Reconsidering repre- sentation alignment for multi-view clustering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1255-1265, 2021.
Daniel Trosten,
Sigurd Lokse, Robert Jenssen, and Michael C Kampffmeyer. On the effects of self-supervision and contrastive alignment in deep multi-view clustering. In Proceedings of the JEEE/CVF conference on computer vision and pattern recognition, pp. 23976 23985, 2023.
Xinhang Wan, Jiyuan Liu, Hao Yu, Qian Qu, Ao Li, Xinwang Liu, Ke Liang, Zhibin Dong, and En Zhu. Contrastive continual multiview clustering with filtered structural fusion. IEEE Transactions on Neural Networks and Learning Systems, 2024.
Bocheng Wang, Jiawei Wang, Chusheng Zeng, and Mulin Chen. Locality-driven flexible consensus graph learning for multi-view clustering. Pattern Recognition, pp. 111853, 2025.
Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. On deep multi-view representation learning. In International conference machine learning, pp. 1082-1092. PMLR, 2015.
Jie Wen, Gehui Xu, Zhanyan Tang, Wei Wang, Lunke Fei, and Yong Xu. Graph regularized and feature aware matrix factorization for robust incomplete multi-view clustering. IEEE Transactions Circuits and Systems for Video Technology, 34(5)3378--371,, 2023.
12

---

Published as a conference paper at ICLR 2026
Jie Wen, Jiang Long, Xiaohuan Lu, Chengliang Liu, Xiaozhao Fang, and Yong Xu. Partial multiview incomplete multilabel learning via uncertainty-driven reliable dynamic fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 48(1):236-22) 250, 2026.
Song Wu, Yan Zheng, Yazhou Ren, Jing He, Xiaorong Pu, Shudong Huang, Zhifeng Hao, and Lifang He. Self-weighted contrastive fusion for deep multi-view clustering. IEEE Transactions on Multimedia, 26:9150-99 011009991.220111 2024.
Baili Xiao, Zhibin Dong, Ke Liang, Suyuan Liu, Siwei Wang, Tianrui Liu, Xingchen Hu, En Zhu, and Xinwang Liu. Easemve: Efficient dual selection mechanism for deep multi-view clustering. In Proceedings of the Computer Vision and Puttern Recognition Conference, pp. 20716-20726, 2025.
Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking 2017.
machine learning algorithms. arXiv preprint
Yuan Xie, Bingqian Lin, Yanyun Qu, Cuihua Li, Wensheng Zhang, Lizhuang Ma, Yonggang Wen, and Dacheng Tao. Joint deep multi-view learning for image clustering. IEEE Transactions on Knowledge and Data Engineering, 33(11 (1:11)):5444460) 2020.
Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, and Lifang He. Multi-level feature learning for contrastive multi-view clustering. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 16051-16060, 2022.
Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Hengtao Shen, Gang Niu, and Xiaofeng Zhu. Self- weighted contrastive learning among multiple views for mitigating representation degeneration. Advances in neural information processing systems, 36:1 19-1119-11111 2023.
Jie Xu, Yazhou Ren, Xiaolong Wang, Lei Feng, Zheng Zhang. Gang Niu, and Xiaofeng Zhu. Investigating and mitigating side effects of noisy views self-supervised clustering algorithms in practical multi-view scenarios. In Proceedings of the IEEF/CVF conference computer vision and pattern recognition, pp. 22957-22966, 2024.
Junpeng Xu, Min Meng. Jigang Liu, and Jigang Wu. Deep mue clustering with diverse and discriminative feature learning. Pattern Recognition, 161:111322, 2025.
Weiqing Yan, Yuanyang Zhang, Chenlei Lv, Chang Tang, Guanghui Yue, Liang Liao, and Weisi Lin.
Gefagg: Global and cross-view feature aggregation for multi-view clustering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 19863-19872, 2023.
Weiqing Yan, Tingyu Yang, and Chang Tang. Self-supervieed semantic soft label learning network for deep multi-view clustering. IEEE Transactions on Multimedia, 2025.
Yugen Yi, Ningyi Zhang. Zehui Zhang, Yijian Fu, Lei Chen, and Jianzhong Wang. Emifel: An efficient multilevel fusion contrastive learning for multiview clustering. IEEE Transactions on Neural Networks and Learning Systems, 2025.
Yongbo Yu, Zhoumin Lu, Feiping Nie, Weizhong Yu, Zongeheng Miao, and Xuelong Li. Pseudo-label guided bidirectional discriminative deep multi-view subspace clustering. IEEE Transactions on Knowledge and Data Engineering, 2025.
13


Reviews:
[{"review_id": "x02kx5vPdp", "reviewer": "Reviewer_UBJs", "summary": "This paper proposes two modules—relationship alignment and view-aware weighting—that preserve the neighborhood structure and alleviate the representation conflict caused by low view similarity, demonstrating a certain level of innovation.", "questions": "1. This paper introduces two innovative modules. Please explain and describe the inherent connection between these two modules.\n2. Sections 2.1 and 2.2 still lack sufficient research and discussion on related work. Please further enrich this section.\n3. This paper cites two studies related to view weighting in the related work section (Xu et al. (2023) and Wu et al. (2024)), yet the experimental comparison only includes a performance comparison with the SEM method. It is recommended to add a comparative experiment with the study by Wu et al. (2024).\n4. This paper uses the Gaussian kernel function to compute the relationship matrix between samples. Please explain the reason for choosing the Gaussian kernel instead of using Euclidean distance-based methods to compute the relationship matrix.\n5. When the similarity between two views is extremely low, the corresponding weight is significantly reduced, potentially leading to excessive suppression of the view pair's contribution and the loss of limited yet valuable discriminative information from that view. Does the proposed method suffer from this issue?\n6. In the Implementation Details section, it is recommended to include the dimensionality settings of the encoder's hidden layers, and to specify whether a consistent network structure was used across all datasets.", "limitations": "", "rating": 6, "confidence": 1, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The experiment compares multiple methods on different datasets, and the comparison methods are comprehensive. \n2. The ablation study not only validates the effectiveness of each loss component but also further verifies the effectiveness of the view-aware adaptive weighting.\n3. A view-aware adaptive weighting mechanism based on Wasserstein distance is introduced to dynamically adjust the weight of view pairs in label contrastive learning, avoiding the forced alignment of inconsistent views.\n4. This paper focuses on sample neighborhood structure consistency rather than traditional sample-level consistency.", "weaknesses": "1. The description in the introduction of this paper has issues with insufficient coherence, and the logical flow needs improvement.\n2. This paper does not clearly specify whether the contrastive method strictly follows the configuration in the original paper, lacking necessary details.\n3. The conclusion section is highly repetitive of the abstract, failing to explore the core value of the innovations in depth, and lacks a thorough analysis and summary of the research findings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "j5g2k0LqEF", "reviewer": "Reviewer_Jhbm", "summary": "This paper proposes two key core modules: first, aligning global and local relation matrices to maintain neighborhood consistency; second, dynamically adjusting the weights of view pairs based on WD to alleviate representation conflicts. Meanwhile, the experimental section thoroughly validates the effectiveness of the proposed method.", "questions": "1.This paper concatenates the features of all views to obtain $Z∈R^{N×V_d}$, and further constructs the global relation matrix. Compared to other fusion strategies (such as weighted fusion of features to construct the relation matrix), what are the advantages of choosing this strategy?\n\n2.Does constructing the relation matrix lead to an increase in computational cost, creating certain limitations?\n\n3.The weighting mechanism only applies to the label contrastive loss, while in the relation alignment module, all views are equally aligned with the global matrix. Could this be considered an inconsistent treatment?\n\n4.Some descriptions in the paper are somewhat colloquial. It is recommended to revise them into a more formal and academic expression.\n\n5.The weight between views is calculated as $1/2\\(w_{v,u}+w_{u,v}\\)$ in equation (12). Please explain why this representation is used.\n\n6.Algorithm 1 is described concisely but lacks key details, such as how the relation matrix is obtained.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.This paper not only considers the structural relationships between samples but also employs a more universal metric to assess the similarity between views.\n\n2.This paper uses three major clustering evaluation metrics: ACC, NMI, and PUR. The experimental results are presented clearly, and the performance is highly convincing.\n\n3.The conclusion not only summarizes the core aspects of the method but also offers suggestions for future research directions.", "weaknesses": "1.This paper does not provide the rationale for selecting the contrastive learning temperature parameters $τ_F$ and $τ_L$.\n\n2.Algorithm 1 provides an overview of the training process but lacks details of the key steps.\n\n3.The descriptions in the introduction are somewhat repetitive and redundant.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "8HCknW8Cdw", "reviewer": "Reviewer_jE16", "summary": "This paper proposes a multi-view clustering method that combines relational alignment and view-aware weighting, addressing two core challenges in the field: insufficient preservation of sample neighborhood structure and adaptive utilization of view similarity. The experimental design is rigorous and logically clear, providing empirical support for the effectiveness of the proposed method.", "questions": "1. The paper introduces view-aware adaptive weighting, particularly the Wasserstein distance-based view similarity measurement. Please provide a detailed explanation of the advantages of Wasserstein distance in handling distributional differences.\n2. The paper lacks visualizations of the feature space or clustering results, such as t-SNE plots, making it difficult to intuitively appreciate the improvements achieved by the proposed method in representation learning.\n3. The formatting of mathematical symbols in the paper requires careful attention, such as \\mathbf{q}_{ij}^v.\n4. The conclusion summarizes the key points of the paper and outlines future research directions. Please further elaborate on its research value for future work from both theoretical and practical perspectives.\n5. The current abstract is clear in its writing but somewhat flat in expression. It does not fully highlight the innovation of the research. It is recommended to make the core contributions more prominent and to clearly explain the inherent connection between the two innovative points.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. It clearly points out the shortcomings of existing methods in preserving sample neighborhood structure and adaptively utilizing view similarity.\n2. The relational alignment module and the view-aware weighting mechanism are ingeniously designed, and their combination exhibits strong synergistic effects.\n3. The experimental design of this paper is well-structured and thoroughly validated.\n4. It breaks away from the traditional feature alignment approach, shifting towards deeper structural relationship alignment. By introducing distributional distance awareness in contrastive learning, it avoids the semantic disruption caused by forced alignment.", "weaknesses": "1. There is an issue with inconsistent formatting of mathematical symbols, which should be carefully reviewed to enhance the reader's understanding of the work.\n2. The discussion of future prospects in the conclusion section is not sufficiently detailed.\n3. The description of the innovations is not particularly prominent.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "7zMJqHoaBl", "reviewer": "Reviewer_R3ke", "summary": "The approach is well-motivated, combining relation alignment with view similarity measurement to offer a novel perspective for addressing semantic degradation caused by view discrepancies in multi-view clustering.", "questions": "1. In terms of references, it is recommended that the author include citations to more relevant literature, especially recent studies in the field, to enhance the completeness and reference value of the paper.\n2. Although the experimental data is already comprehensive, it is recommended to analyze in more depth the performance of the method on different datasets, particularly the reasons why it performs better or worse on certain datasets.\n3. Although the paper mentions the differences between this method and the SEM method, the explanation is still not clear enough. Please further elaborate on the core differences between the two.\n4. This paper primarily uses the Wasserstein Distance (WD) to quantify the differences between views. It would be useful to explain why the WD was chosen as the metric, rather than other common metrics like MMD or cosine similarity.\n5. In the calculation of the relationship matrix, the paper does not specify the value of the Gaussian kernel bandwidth σ, nor does it indicate whether σ was adaptively adjusted for different datasets.\n6. In Equation (6), the parameters τ_F and τ_L are mentioned, but their specific meanings are not clearly defined. If the lack of clarity is due to a presentation issue or if they carry special significance, further explanation is needed to enhance the clarity and accuracy of the equation.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. This paper effectively addresses two core issues in current deep multi-view clustering: the weak preservation of neighborhood structure and the representation degeneration caused by view discrepancies. It directly offers solutions to these problems by introducing two modules: cross-view relationship alignment and view-aware label contrastive learning. The former ensures the consistency of the neighborhood structure through alignment, while the latter dynamically adjusts the inter-view weights based on the Wasserstein distance. The relational alignment module and the view-aware weighting mechanism are ingeniously designed, and their combination exhibits strong synergistic effects.\n2. The experiments cover multiple datasets, involving different types, sample sizes, and numbers of views, fully showcasing the diversity of the data.\n3. The experiments include sensitivity analysis of parameters, convergence analysis, and ablation studies, validating the effectiveness of the proposed method from multiple perspectives.", "weaknesses": "1. This paper has some shortcomings in terms of literature coverage in the relevant field, as it fails to comprehensively showcase the depth of the research.\n2. The description of the experimental details is insufficient, such as the lack of clarification on the choice of the Gaussian kernel bandwidth σ.\n3. The description of the formulas is somewhat confusing, particularly in the case of Equation (6).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)