Title: Learning from Disjoint Views: A Contrastive Prototype Matching Network for Fully Incomplete Multi-View Clustering
Venue: NeurIPS 2025
Accepted: yes
Keywords: multi-view learning

deep clustering

fully incomplete information
Abstract: Multi-view clustering aims to enhance clustering performance by leveraging information from diverse sources. However, its practical application is often hindered by a barrier: the lack of correspondences across views. This paper focuses on the understudied problem of fully incomplete multi-view clustering (FIMC), a scenario where existing methods fail due to their reliance on partial alignment. To address this problem, we introduce the Contrastive Prototype Matching Network (CPMN), a novel framework that establishes a new paradigm for cross-view alignment based on matching high-level categorical structures. Instead of aligning individual instances, CPMN performs a more robust cluster prototype alignment. CPMN first employs a correspondence-free graph contrastive learning approach, leveraging mutual $k$-nearest neighbors (MNN) to uncover intrinsic data structures and establish initial prototypes from entirely unpaired views. Building on the prototypes, we introduce a cross-view prototype graph matching stage to resolve category misalignment and forge a unified clustering structure. Finally, guided by this alignment, we devise a prototype-aware contrastive learning mechanism to promote semantic consistency, replacing the reliance on the initial MNN-based structural similarity. Extensive experiments on benchmark datasets demonstrate that our method significantly outperforms various baselines and ablation variants, validating its effectiveness.

Learning from Disjoint Views: A Contrastive Prototype Matching Network for Fully Incomplete Multi-View Clustering

Abstract

Multi-view clustering aims to enhance clustering performance by leveraging information from diverse sources. However, its practical application is often hindered by a barrier: the lack of correspondences across views. This paper focuses on the understudied problem of fully incomplete multi-view clustering (FIMC), a scenario where existing methods fail due to their reliance on partial alignment. To address this problem, we introduce the Contrastive Prototype Matching Network (CPMN), a novel framework that establishes a new paradigm for cross-view alignment based on matching high-level categorical structures. Instead of aligning individual instances, CPMN performs a more robust cluster prototype alignment. CPMN first employs a correspondence-free graph contrastive learning approach, leveraging mutual k -nearest neighbors (MNN) to uncover intrinsic data structures and establish initial prototypes from entirely unpaired views. Building on the prototypes, we introduce a cross-view prototype graph matching stage to resolve category misalignment and forge a unified clustering structure. Finally, guided by this alignment, we devise a prototype-aware contrastive learning mechanism to promote semantic consistency, replacing the reliance on the initial MNN-based structural similarity. Extensive experiments on benchmark datasets demonstrate that our method significantly outperforms various baselines and ablation variants, validating its effectiveness.

1 Introduction

Over the recent years, the rapid increase in multi-view data has made effective analysis an urgent necessity[17, 15]. Multi-view clustering, which seeks to uncover underlying data relationships by partitioning instances into distinct categories without supervision, has emerged as a prominent solution. While these algorithms have advanced significantly and are widely applied in applications like recommendation systems and multimedia analysis [26, 38, 37, 29], most existing algorithms heavily rely on the assumption of complete multi-view information [3, 33]. However, in practice, it is

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common for only partial data to be collected or transmitted due to sensor failure, varying acquisition costs, or privacy concerns, making the assumption of data completeness difficult to satisfy.

To address the issue of incomplete data, many efforts have been made to explore ways of understanding multi-view data from partially incomplete information (Figure 1a), which is referred to as partially incomplete multi-view clustering (PIMC). The core mechanism of PIMC lies in leveraging available partial cross-view correspondences to establish informative priors for learning. Two principal approaches have arisen based on the differing conditions of data incompleteness. For multi-view data with partially missing views, existing methods attempt to exploit known cross-view correspondences to learn shared structures and recover missing information in the latent space [15, 1]. Alternatively, for partially aligned multi-view data, existing approaches aim to learn optimal transformations or permutation matrices that align the different view-specific spaces, establishing links between matching views [35, 28].

While these methods successfully mitigate the impact of partially incomplete information, their effectiveness fundamentally relies on the availability of at least partial cross-view information or alignment, which is often difficult to guarantee [41]. In many challenging real-world scenarios, data from different views is collected, processed, and stored entirely separately. Due to data heterogeneity, privacy regulations, or the nature of data acquisition, establishing correspondences even for a small subset of instances can be infeasible. This leads to the critical problem of fully incomplete multi-view data, where instances are characterized by data from only one view, and no cross-view correspondences are known a priori (Figure 1b). An example is in clinical diagnostics, where patients may undergo only one type of pathological examination, resulting in single-view information with no link between patients across different test types. Tackling this scenario requires learning shared cluster structures without any correspondences between views.

The Fully Incomplete Multi-view Clustering (FIMC) presents significant algorithmic challenges compared to PIMC. Unlike PIMC, where known correspondences provide direct guidance for imputation or alignment, the fully incomplete scenario lacks cross-view anchors that could guide representation alignment. Consequently, imputation approaches based on cross-view correspondences are inapplicable, and alignment methods relying on paired instances cannot be initialized or directly learned. Recently, a limited number of pioneering studies have started to tackle the FIMC challenge. Notably, SMILE [41] focuses on learning semantic invariant representations for FIMC. This approach effectively mitigates the absence of correspondences by ensuring that instances belonging to the same underlying cluster have similar representations regardless of their view. However, this approach assumes that semantic distributions are strictly invariant across views, which may fail under noisy views or class imbalance, leading to biased alignment. This motivates the exploration of methods

that explicitly handle the alignment of cluster prototypes and utilize category-level information for representation refinement once initial cluster structures emerge.

This paper proposes the novel Contrastive Prototype Matching Network (CPMN) to introduce a new paradigm of structure-level alignment to address FIMC. Instead of aligning instances or enforcing rigid distributional invariance, CPMN operates at a higher level of abstraction by aligning robust categorical prototypes ( (Figure 1c). Our approach first employs a correspondence-free cross-view graph contrastive learning strategy, utilizing mutual k -nearest neighbors (MNN) to learn initial representations and generate view-specific prototypes via K-means. Crucially, we then explicitly bridge the view-specific cluster structures by introducing a similarity-based prototype graph matching to find the optimal mapping between these initial prototypes. Finally, CPMN refines the representations and prototypes through a prototype contrastive learning mechanism, which leverages the established prototype correspondences and category information to contrast instances against their matched prototypes. The key contributions of our work include:

Wepropose a novel framework, Contrastive Prototype Matching Network (CPMN), designed to address the challenging fully incomplete multi-view clustering problem where no crossview correspondences exist.

We introduce a two-stage contrastive learning approach: an initial correspondence-free graph contrastive learning phase based on cross-view MNN for structural alignment, followed by a prototype contrastive learning phase that utilizes category-level information derived from matched prototypes for semantic refinement.

We incorporate an explicit prototype graph matching strategy to construct a bipartite graph between view-specific prototypes and find the optimal mapping between prototypes based on their feature similarity.

2 Related Works

Before introducing the proposed CPMN, we briefly review the related partially missing multi-view clustering and partially aligned multi-view clustering.

Partially Missing Multi-view Clustering (PMMC) aims to learn unified cluster assignments from multi-source data where some views are partially missing for certain instances. Among the methods that broadly employ either conventional shallow models [27, 18] or deep neural networks (DNN) [30, 1], our attention here is restricted to the DNN-based approaches. Owing to the capability of the autoencoder (AE) to handle missing data through feature reconstruction, prevailing methods generally adopt AE as their backbone. Some approaches combine AE with graph information to mitigate the shortcomings of structural information mining in AE. For example, SDIMC-net [31] combines graph embedding strategies to capture the high-level features and local structure of each view. Likewise, CASEN [34] performs adaptive graph convolution on the reconstructed multi-view data to effectively extract the data structure. Some approaches introduce contrastive learning [8, 2] to learn a shared representation that is robust to view missingness. DCP [15] performs data recovery and consistency learning simultaneously through dual contrastive prediction. Introducing deep subspace learning, PVC-SSN [4] employs contrastive learning to obtain more discriminative and consistent subspace representations. DCMVSC [40] combines contrastive learning and CauchySchwarz divergence to propose a new multi-view deep subspace clustering approach. While existing approaches demonstrate improved robustness to incomplete data, their dependence on at least partial cross-view correspondences persists as a limitation, particularly in scenarios without alignment.

Partially Aligned Multi-view Clustering (PAMC) addresses the critical challenges of view misalignment and unknown correspondences caused by asynchronous data acquisition in multi-view clustering. Existing methods can be broadly classified into two categories. The first category focuses on explicit alignment learning, while the second category focuses on robust representation learning without explicit alignment. As a pioneering study, PVC [10] addresses the partial alignment problem by proposing a differentiable Hungarian network to find correspondences between two views. Distinctly, EGPVC [42] employs Dykstra's cyclic constraint projection algorithm for the same purpose. However, explicit instance-level alignment in such methods is sometimes difficult to achieve and is overly restrictive for clustering tasks. To learn robust representation without explicit alignment, MvCLN [36] devises a noise-robust contrastive loss to reduce the impact of false negative pairs on aligning data and representation learning. SiMVC [25] completely avoids explicit representation alignment

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and instead uses learned linear combinations for view prioritization. The above methods achieve expected performance, but their alignment processes, which are often applicable only to two views or necessitate partial alignment information, consequently limit their broader applicability.

3 Method

In this section, we elaborate on the proposed Contrastive Prototype Matching network (CPMN) for learning a unified clustering partition from fully incomplete multi-view data. As illustrated in Fig. 2, CPMN consists of three novel modules, namely correspondence-free graph contrastive learning, prototype graph matching, and prototype contrastive learning, which will be detailed in Sec. 3.1-3.3

In fully incomplete multi-view clustering, paired information across views is absent. That is, all instances have features from only one view, or the correspondences between views are completely unknown. Formally, given a fully incomplete multi-view dataset X = { X ( v ) } V v =1 with N instances in V views. X ( v ) = { x ( v ) 1 , ..., x ( v ) N } ∈ R N × d v denotes the feature matrix in the v -th view, where d v is the feature dimension. For instances with only one view, we introduce an indicator matrix W ∈ { 0 , 1 } N × V to indicate the completeness of views, where W i,v = 1 if instance x i exists in the v -th view, and W i,v = 0 otherwise. Thus, for any instance x i of a fully incomplete dataset, ∑ V v =1 W i,v = 1 . For datasets without known correspondences, an indicator vector U ∈ { 0 , 1 } N is used to record the aligned instances, where U i = 1 means the i -th instance is aligned in all views, otherwise U i = 0 . Thus, for a fully incomplete dataset, U i = 0 for all i ∈ [1 , N ] . Our goal is to partition these N instances into C clusters.

3.1 Correspondence-Free Graph Contrastive Learning

To effectively address the challenges of FIMC, the initial crucial step in our CPMN is to learn latent representations for each view independently, while simultaneously encouraging cross-view consistency without relying on paired data. This stage, termed correspondence-free graph contrastive learning, is designed to uncover underlying structural similarities across views. It achieves this by first reconstructing individual view data to preserve view-specific information, and then employing a novel graph-based contrastive learning strategy to promote coherence between the learned view-specific representations, thereby laying a foundation for subsequent prototype generation and matching.

A key component for learning these initial view-specific representations is the foundational backbone network: view-specific autoencoders. For each view v , the architecture comprises an encoder E v : R d v → R d z and a corresponding decoder D v : R d z → R d v . The encoder maps the input data x ( v ) i to a low-dimensional latent representation z ( v ) i = E v ( x ( v ) i ) , and the decoder attempts to reconstruct the original input ˆ x ( v ) i = D v ( z ( v ) i ) . The model is trained by minimizing the weighted

reconstruction error for observed instances in each view, formulated as:

While view-specific autoencoders can capture latent features within individual views, they do not inherently model cross-view relationships, resulting in divergent latent spaces that impede view alignment. Prevailing cross-view contrastive learning in multi-view clustering typically addresses this using cross-view instance pairs [16, 6, 22]. However, the absence of view correspondences in FIMC renders such pairwise instance selection infeasible. To overcome this limitation, we propose a correspondence-free graph contrastive learning strategy based on mutual k -nearest neighbors (MNN), which establishes pseudo-alignment through feature affinity without relying on prior correspondences. To learn discriminative representations while simultaneously mitigating cross-view discrepancies, CPMN involves the separate construction of intra-view and cross-view MNN graphs, which subsequently guide the establishment of positive pairs.

For intra-view consistency, positive pairs are identified based on the MNN criterion applied to the latent representations. Specifically, a pair of representations ( z ( v ) i , z ( v ) j ) from the same view v is designated as positive if z ( v ) j is found among the k -nearest neighbors of z ( v ) i , and conversely, z ( v ) i is also present among the k -nearest neighbors of z ( v ) j . All other pairs within this view are treated as negative pairs. To quantify the affinity between these pairs, cosine similarity is utilized:

The objective is to pull together the representations of connected samples in the graph while pushing apart unconnected ones. Thus, the loss function can be formulated as:

̸

where P ( v ) i is the set of indices of instances forming positive pairs with z ( v ) i within view v , and τ is the temperature parameter.

Complementing the intra-view learning, cross-view positive pairs are established between different views, enabling alignment without correspondences. Considering a two-view example, positive pairs ( z ( v a ) i , z ( v b ) j ) are similarly established if they are MNNs of each other across views v a and v b . All other pairings between these views are treated as negative pairs. Cosine similarity is again employed to measure the similarity, denoted as S ( v a ,v b ) ij = sim ( z ( v a ) i , z ( v b ) j ) . Cross-view graph contrastive learning aims to uncover and align latent structures, thereby achieving partial representation consistency despite the absence of correspondence.The loss function takes a similar form to the single-view contrastive loss:

where P ( v a ,v b ) i is the set of indices of instances from view v b forming positive pairs with z ( v a ) i .

The overall objective for this initial stage of correspondence-free graph contrastive learning, denoted as L CFGC, integrates the aforementioned losses:

̸

where λ is a trade-off parameter between the two losses.

Following the representation learning guided by L CFGC, initial view-specific cluster structures are derived. Specifically, K-Means is applied to the latent representations Z ( v ) to obtain preliminary clustering assignments and a set of prototypes C ( v ) ∈ R C × d z . These prototypes are crucial for the subsequent graph matching.

3.2 Prototype Graph Matching

Following the correspondence-free contrastive learning, we successfully obtain view-specific representations and initial cluster prototypes for each view. However, these prototypes are independently derived and thus lack explicit cross-view correspondences. To bridge this gap, we devise a prototype graph matching strategy to establish correspondence to enable cross-view learning and fusion.

To establish an alignment between the independently derived sets of prototypes from different views (e.g., view v a and view v b ), we formulate this challenge as a principled optimization problem, specifically the classic Linear Assignment Problem (LAP), which can be modeled via bipartite graph matching. In our model, one set of nodes in the bipartite graph consists of the C prototypes from view v a , denoted C ( v a ) = { c ( v a ) 1 , . . . , c ( v a ) C } , and the other set comprises the C prototypes from view v b , C ( v b ) = { c ( v b ) 1 , . . . , c ( v b ) C } . An edge between a prototype c ( v a ) i and a prototype c ( v b ) j represents a potential correspondence, and the core task is to find the most plausible one-to-one mapping between these two sets.

To quantify the suitability of each potential match, we construct a cost matrix M ( v a ,v b ) ∈ R C × C , where M ( v a ,v b ) ij represents the cost of matching the i -th prototype from view v a , with the j -th prototype from view v b . This cost is based on the dissimilarity between prototypes, which is formulated as:

where c v a i denotes the i -th prototype from view v a .

The goal is to find an optimal permutation of indices, π : C ( v a ) →C ( v b ) , that establishes a one-to-one mapping. This mapping assigns each prototype c ( v a ) i in view v a to a unique prototype c ( v b ) π ( i ) in view v b , such that the cumulative cost of all matches is minimized. This objective can be expressed as:

This formulation guarantees that the problem is solvable in polynomial time. We find the globally optimal solution using the efficient Hungarian algorithm [13]. The output of the algorithm is the optimal matching, providing a mapping from the prototype indices of view v a to those of view v b .

The established correspondence between prototypes creates a vital bridge for transferring clustering information across views. This enables us to incorporate cross-view category information into contrastive learning, using the category information in one view to guide the clustering in another.

3.3 Prototype Contrastive Learning

Leveraging the globally optimal prototype alignment, this stage transitions the learning objective from structure discovery to semantic refinement. It utilizes the matched prototypes as robust categorical anchors to refine both representations and prototypes. By contrasting instances with these anchors, this module elevates the alignment objective from the initial MNN-based structural similarity to a higher-order, globally consistent semantic alignment in the latent space. Additionally, for dynamic updates to cluster assignments and prototypes, a learnable clustering layer is used to replace the previous K-means methodology.

This prototype contrastive learning leverages the prototype mapping π : C ( v a ) →C ( v b ) established previously to guide representation refinement. For a given representation z ( v a ) i , let j be the index of the prototype c ( v a ) j to which it is assigned within view v a . The core idea is to encourage z ( v a ) i to be simultaneously similar to its assigned prototype c ( v a ) j and to the corresponding prototype in view v b ,

c ( v b ) π ( j ) . This objective is realized through two complementary loss components, defined as:

Here, for instance z ( v ) i , the index j in the numerators Sc ( v ) ij and Sc ( v a ,v b ) ij refers to its assigned prototype index for the positive term. Thus, L ( v ) SPL promotes the proximity of each representation z ( v ) i to its assigned prototype c ( v ) j within view v . Concurrently, L ( v a ,v b ) CPL fosters alignment by encouraging z ( v a ) i from view v a to be closer to the cross-view corresponding prototype c ( v b ) π ( j ) in view v b . The terms Sc ( v ) ij = sim ( z ( v ) i , c ( v ) j ) and Sc ( v a ,v b ) ij = sim ( z ( v a ) i , c ( v b ) π ( j ) ) represent the cosine similarity between representations and prototypes, within or across views respectively.

To further enhance cluster coherence and embedding discriminability, a self-supervision strategy inspired by [7] is integrated within the learnable clustering layers. For each instance z ( v ) i , q ( v ) ik denotes its soft assignment to prototypes c ( v ) k , which is computed using the Student's t-distribution [20] as:

Subsequently, an auxiliary target distribution p ( v ) ik is formulated by sharpening q ( v ) ik as:

The clustering refinement is then driven by minimizing the Kullback-Leibler (KL) divergence between the current soft assignment distribution Q ( v ) and the target distribution P ( v ) for each view:

where P ( v ) i and Q ( v ) i are the target distribution and current soft assignment vectors for instance z ( v ) i , respectively, and D KL denotes the KL divergence.

The overall optimization objective for the prototype contrastive learning stage is a weighted sum of the clustering loss and the prototype contrastive losses:

̸

where γ is a trade-off parameter between the two losses. Joint optimization of these terms produces discriminative representations and refined prototypes, culminating in a robust clustering solution for fully incomplete multi-view data.

4 Experiments

In this section, we conduct experiments on five popular multi-view datasets to evaluate the effectiveness of the proposed CPMN.

4.1 Experimental Settings

Datasets Five widely-used datasets are used to evaluate the proposed CPMN. (1) Caltech-101 is an image dataset containing 8677 instances from 101 categories. Following [41], deep features extracted by DECAF [12] and VGG19 [21] are used as two views. (2) Cub includes several categories of

birds. Following [41], visual features extracted by GoogLeNet [23] and text features extracted by doc2vec [14] are used as two views. (3) NoisyMNIST includes 70,000 instances, each providing two views: the raw MNIST image and a rotated, Gaussian-noised variant. Following [41], 30, 000 instances are randomly selected for evaluation. (4) MNIST-USPS contains 67,291 digit images from the MNIST and USPS datasets. As in a prior study [41], we randomly sample 5,000 items from each dataset, covering all 10 digits. (5) YouTubeFaces features 152,549 faces across 66 identities, with each identity having over 1,500 images. For comparisons, we follow prior work [41] to describe each image using multi-view features, specifically a 512-dimensional GIST feature, a 1984-dimensional HOG feature, and a 1024-dimensional HIST feature.

Baselines We compared CPMN with 12 multi-view clustering baselines. These baselines can be divided into the following categories based on the type of data they handle. Four baselines are designed to handle partially missing multi-view data, including DAIMC [9], EERIMVC [19], DCP [15], and DSIMVC [24]. PVC [10] and MvCLN [36] are designed for partially unaligned data. SURE [35] can address both of the aforementioned partial incompleteness problems. Distinctly, MVC-UM [39], GWMAC [5], and UPMGC-SM [32] are designed to handle fully unaligned multi-view data. DM2C [11] is designed to handle fully missing multi-view data. SMILE [41] can address both of the aforementioned problems. For fairness, we only show the clustering results of various methods on the incomplete data they can process.

Implementation Details We implement the proposed CPMN via the PyTorch public toolboxes, and we conduct the experiment on a server running Ubuntu Linux 20.04 that is equipped with an Intel(R) Xeon(TM) W5-3425 CPU @ 3.20 GHz, NVIDIA RTX 4090 Graphics Processing Units (GPUs), and 128 GB of memory. Following previous studies [35, 41], we define the missing rate as ψ = m/N , where m is the number of instances with missing views. To generate incomplete data, we randomly select m instances and drop one view for each instance. Similarly, we define the unaligned rate as ζ = c/N , where c is the number of instances without correspondences. We randomly sample c instances and remove the correspondence between their views.

4.2 Comparison with Baselines

Table 1 provides a comprehensive quantitative comparison of CPMN against several multi-view clustering methods under two challenging fully incomplete scenarios: 100% unaligned instances and 100% missing views. The experimental results lead to several key observations. Firstly, our CPMN outperforms baselines across nearly all datasets, a success attributed to its novel prototype matching-based view alignment strategy. Secondly, with the unaligned rate of 100%, both CPMN and SMILE generally exhibit superior performance over other unaligned multi-view clustering methods, benefiting from the latent feature extraction capabilities of DNN. Conventional methods like UPMGCSM achieve competitive performance, suggesting potential limitations of DNN-based approaches in capturing essential data distributions within smaller-scale datasets.

Furthermore, to comprehensively evaluate the robustness of CPMN, its clustering performance is also tested under partially incomplete scenarios. Table 2 presents the clustering results for CPMN alongside several baseline methods under scenarios of 50% unaligned instances and 50% missing views. The results indicate that even though CPMN utilizes no view correspondence information and employs view-specific autoencoders without weight sharing, it still achieves top-two performance across most datasets. Notably, it attains the best overall results on Caltech-101 and YouTubeFaces, leading in at least two metrics per dataset. These results further demonstrate the effectiveness of CPMN and its capability to generalize to partially incomplete settings.

To further evaluate the robustness of CPMN against the strongest baseline, SMILE, we conducted a detailed analysis on the Caltech-101 dataset with different missing view rates. Table 3 presents the clustering results under missing view rates from 20% to 100%. From the results, we can observe that: (1) At lower missing rates (20%-40%), where substantial paired information is still available, SMILE shows a slight advantage. Notably, our CPMN achieves highly competitive performance even in this scenario, for which it was not specialized, underscoring the general effectiveness of its correspondence-free framework. (2) Crucially, as the missing rate reaches 60% and paired information becomes scarce, SMILE's performance begins to degrade sharply. In contrast, CPMN's performance remains remarkably stable, surpassing SMILE. This trend intensifies as the problem difficulty increases (80%-100%), clearly demonstrating the superior robustness of CPMN's matching paradigm precisely in the challenging scenarios.

4.3 Ablation Studies and Parameter Analysis

To verify the significance of each component in CPMN, we perform ablation studies to isolate the effect of the reconstruction loss L REC , graph contrastive loss L GL , prototype contrastive loss L PL , and clustering loss L CLU . The clustering results on the Caltech-101 dataset with 100% unalignment and 100% missing views are shown in Table 4. From the results, one could observe that: (1) With the addition of components, the clustering performance improves significantly, and the best clustering performance can be achieved when using all loss terms. (2) L REC plays an essential role in the AEs

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and is therefore retained. Augmenting this with L GL led to a notable enhancement. (3) Building upon the L REC + L GL baseline, the introduction of either L PL and L CLU can further improve performance. This underscores the complementary benefits of these terms in the overall model.

There are two tunable hyper-parameters, namely, λ and γ , which impact the weights of various loss terms. To better illustrate the stability of CPMN, we conducted experiments to investigate the sensitivity of the proposed method to these two hyper-parameters. Figure 3a-3b shows the ACC values versus the hyper-parameters λ and γ on the Caltech-101 dataset with 100% unalignment and 100% missing views. The results show that the CPMN maintains relatively stable performance across a considerable range of these hyper-parameter settings within both incomplete scenarios. When λ and γ are selected from the ranges of [1e-3, 1] and [1e-2, 1], CPMN can achieve desirable performance.

The dimensionality of the latent space d z is a critical hyper-parameter in deep clustering models, exerting a considerable influence on clustering performance. To examine the effect of d z , a sensitivity analysis is conducted on the Caltech-101 dataset with d z ∈ [8 , 128] . Figure 3c shows the ACC values versus d z with 100% unalignment and 100% missing views. It can be observed that ACC increases significantly as d z is raised from 8 to 16. Subsequently, clustering performance gradually declines with further increases in d z . This phenomenon occurs because higher dimensionalities may introduce noise that adversely affects clustering, whereas lower dimensionalities might fail to capture the underlying structure.

5 Conclusion

In this paper, we propose the Contrastive Prototype Matching Network (CPMN), a novel framework that establishes a new structure-level alignment paradigm for fully incomplete multi-view clustering (FIMC). CPMN pioneers learning consistent global clustering by explicitly aligning prototypes across views. Initially, a correspondence-free graph contrastive learning stage with MNN establishes viewspecific prototypes from entirely unpaired views. Subsequently, a crucial similarity-based prototype graph matching step explicitly aligns these prototypes, resolving category discrepancies and forging a unified clustering structure across views. Building on this alignment, CPMN employs a prototypeaware contrastive learning mechanism that leverages matched prototypes and emerging category information to refine representations for enhanced semantic consistency. Extensive experiments demonstrate that CPMN achieves superior performance compared to various baselines.

Despite its success, we acknowledge avenues for future work. The performance of CPMN is theoretically linked to the quality of the initial prototypes, suggesting further research into the convergence properties of the joint optimization. Furthermore, our method assumes a predefined cluster count C . Extending this structural alignment paradigm to a non-parametric setting, where C is dynamically inferred from the data, remains a significant and open research direction.

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 62402235, 62276143 and 62272035, and the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications under Grant NY223165.

References

Guoqing Chao, Yi Jiang, and Dianhui Chu. Incomplete contrastive multi-view clustering with highconfidence guiding. In Proc. AAAI Conf. Artif. Intell. (AAAI) , pages 11221-11229, 2024.

Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. A simple framework for contrastive learning of visual representations. In Proc. Int. Conf. Mach. Learn. (ICML) , volume 119, pages 1597-1607, 2020.

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 Proc. Int. Conf. Comput. Vis. (ICCV) , pages 19383-19394, 2023.

Wei Feng, Guoshuai Sheng, Qianqian Wang, Quanxue Gao, Zhiqiang Tao, and Bo Dong. Partial multi-view clustering via self-supervised network. In Proc. AAAI Conf. Artif. Intell. (AAAI) , pages 11988-11995, 2024.

Fengjiao Gong, Yuzhou Nie, and Hongteng Xu. Gromov-wasserstein multi-modal alignment and clustering. In Proc. ACM Int. Conf. on Inform. & Knowl. Management (CIKM) , pages 603-613, 2022.

Ruiming Guo, Mouxing Yang, Yijie Lin, Xi Peng, and Peng Hu. Robust contrastive multi-view clustering against dual noisy correspondence. Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , 37:121401-121421, 2024.

Xifeng Guo, Long Gao, Xinwang Liu, and Jianping Yin. Improved deep embedded clustering with local structure preservation. In Proc. Int. Joint Conf. Artif. Intell. (IJCAI) , volume 17, pages 1753-1759, 2017.

Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. Momentum contrast for unsupervised visual representation learning. In Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , pages 9726-9735, 2020.

Menglei Hu and Songcan Chen. Doubly aligned incomplete multi-view clustering. In Proc. Int. Joint Conf. Artif. Intell. (IJCAI) , pages 2262-2268, 2018.

Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, and Xi Peng. Partially view-aligned clustering. In Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , 2020.

Yangbangyan Jiang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, and Qingming Huang. DM2C: deep mixed-modal clustering. In Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , pages 5880-5890, 2019.

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Communications of the ACM , 60(6):84-90, 2017.

Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly , 2(1-2):83-97, 1955.

Quoc Le and Tomas Mikolov. Distributed representations of sentences and documents. In Proc. Int. Conf. Mach. Learn. (ICML) , pages 1188-1196, 2014.

Yijie Lin, Yuanbiao Gou, Xiaotian Liu, Jinfeng Bai, Jiancheng Lv, and Xi Peng. Dual contrastive prediction for incomplete multi-view representation learning. IEEE Trans. Pattern Anal. Mach. Intell. , 45(4):4447-4461, 2023.

Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, and Xi Peng. COMPLETER: incomplete multi-view clustering via contrastive prediction. In Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , pages 11174-11183, 2021.

Haoran Liu, Ying Ma, Ming Yan, Yingke Chen, Dezhong Peng, and Xu Wang. Dida: Disambiguated domain alignment for cross-domain retrieval with partial labels. In Proc. AAAI Conf. Artif. Intell. (AAAI) , volume 38, pages 3612-3620, 2024.

Suyuan Liu, Siwei Wang, Ke Liang, Junpu Zhang, Zhibin Dong, Tianrui Liu, En Zhu, Xinwang Liu, and Kunlun He. Alleviate anchor-shift: Explore blind spots with cross-view reconstruction for incomplete multi-view clustering. In Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , 2024.

Xinwang Liu, Miaomiao Li, Chang Tang, Jingyuan Xia, Jian Xiong, Li Liu, Marius Kloft, and En Zhu. Efficient and effective regularized incomplete multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell. , 43(8):2634-2646, 2020.

Laurens Van Der Maaten and Geoffrey Hinton. Visualizing data using t-sne. J. Mach. Learn. Res. , 9(2605):2579-2605, 2008.

Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In Proc. Int. Conf. Learn. Represent. (ICLR) , 2015.

Chao Su, Huiming Zheng, Dezhong Peng, and Xu Wang. Dica: Disambiguated contrastive alignment for cross-modal retrieval with partial labels. In Proc. AAAI Conf. Artif. Intell. (AAAI) , volume 39, pages 20610-20618, 2025.

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , pages 1-9, 2015.

Huayi Tang and Yong Liu. Deep safe incomplete multi-view clustering: Theorem and algorithm. In Proc. Int. Conf. Mach. Learn. (ICML) , volume 162, pages 21090-21110, 2022.

Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, and Michael Kampffmeyer. Reconsidering representation alignment for multi-view clustering. In Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , pages 1255-1265, 2021.

Devesh Walawalkar and Pablo Garrido. Videoclusternet: Self-supervised and adaptive face clustering for videos. In Proc. Eur. Conf. Comput. Vis. (ECCV) , volume 15088, pages 377-396, 2024.

Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, and En Zhu. Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences. In Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , 2022.

Xibiao Wang, Hang Gao, Xindian Wei, Liang Peng, Rui Li, Cheng Liu, Si Wu, and Hau-San Wong. Contrastive graph distribution alignment for partially view-aligned clustering. In Proc. ACM Int. Conf. Multimedia (ACM MM) , pages 5240-5249, 2024.

Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, and Qingming Huang. Openauc: Towards auc-oriented open-set recognition. Proc. Adv. Neural Inform. Process. Syst. (NeurIPS) , 2022.

Jie Wen, Shijie Deng, Waikeung Wong, Guoqing Chao, Chao Huang, Lunke Fei, and Yong Xu. Diffusionbased missing-view generation with the application on incomplete multi-view clustering. In Proc. Int. Conf. Mach. Learn. (ICML) , 2024.

Jie Wen, Zhihao Wu, Zheng Zhang, Lunke Fei, Bob Zhang, and Yong Xu. Structural deep incomplete multi-view clustering network. In Proc. ACM Int. Conf. on Inform. & Knowl. Management (CIKM) , pages 3538-3542, 2021.

Yi Wen, Siwei Wang, Qing Liao, Weixuan Liang, Ke Liang, Xinhang Wan, and Xinwang Liu. Unpaired multi-view graph clustering with cross-view structure matching. IEEE Trans. Neural Networks Learn. Syst. , 2023.

Zichen Wen, Yawen Ling, Yazhou Ren, Tianyi Wu, Jianpeng Chen, Xiaorong Pu, Zhifeng Hao, and Lifang He. Homophily-related: Adaptive hybrid graph filter for multi-view graph clustering. In Proc. AAAI Conf. Artif. Intell. (AAAI) , pages 15841-15849, 2024.

Zhe Xue, Junping Du, Changwei Zheng, Jie Song, Wenqi Ren, and MeiYu Liang. Clustering-induced adaptive structure enhancing network for incomplete multi-view data. In Proc. Int. Joint Conf. Artif. Intell. (IJCAI) , pages 3235-3241. ijcai.org, 2021.

Mouxing Yang, Yunfan Li, Peng Hu, Jinfeng Bai, Jiancheng Lv, and Xi Peng. Robust multi-view clustering with incomplete information. IEEE Trans. Pattern Anal. Mach. Intell. , 45(1):1055-1069, 2023.

Mouxing Yang, Yunfan Li, Zhenyu Huang, Zitao Liu, Peng Hu, and Xi Peng. Partially view-aligned representation learning with noise-robust contrastive loss. In Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , pages 1134-1143, 2021.

Zhiyong Yang, Qianqian Xu, Shilong Bao, Peisong Wen, Yuan He, Xiaochun Cao, and Qingming Huang. Auc-oriented domain adaptation: From theory to algorithm. IEEE Trans. Pattern Anal. Mach. Intell. , 45(12):14161-14174, 2023.

Ziniu Yin, Yanglin Feng, Ming Yan, Xiaomin Song, Dezhong Peng, and Xu Wang. Roda: Robust domain alignment for cross-domain retrieval against label noise. In Proc. AAAI Conf. Artif. Intell. (AAAI) , volume 39, pages 9535-9543, 2025.

Hong Yu, Jia Tang, Guoyin Wang, and Xinbo Gao. A novel multi-view clustering method for unknown mapping relationships between cross-view samples. In Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (KDD) , pages 2075-2083, 2021.

Xuejiao Yu, Yi Jiang, Guoqing Chao, and Dianhui Chu. Deep contrastive multi-view subspace clustering with representation and cluster interactive learning. IEEE Trans. Knowl. Discov. Eng. , 37(1):188-199, 2025.

Pengxin Zeng, Mouxing Yang, Yiding Lu, Changqing Zhang, Peng Hu, and Xi Peng. Semantic invariant multi-view clustering with fully incomplete information. IEEE Trans. Pattern Anal. Mach. Intell. , 46(4):2139-2150, 2024.

Liang Zhao, Qiongjie Xie, Sontao Wu, and Shubin Ma. An end-to-end framework for partial view-aligned clustering with graph structure. In Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP) , pages 1-5, 2023.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Contrastive Prototype Matching Network (CPMN), a novel framework for fully incomplete multi-view clustering. CPMN aims to learn consistent global clustering by explicitly aligning prototypes across views. It integrates three innovative stages: a correspondence-free graph contrastive learning stage, a similarity-based prototype graph matching, and a prototype-aware contrastive learning stage to learn unified clustering across disjoint views. Experiments verify the effectiveness of the proposed CPMN.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n（1）The paper is well-organized.\n\n（2）The problem addressed is novel and the motivation is clear.\n\n（3）The experimental results demonstrate a substantial improvement in fully incomplete multi-view clustering.\n\nWeaknesses\n- Please find the specifics in Questions section.\n\nQUESTIONS:\n（1）CPMN seems to be done in phases, first acquiring initial prototypes, then prototype matching, and finally optimizing the prototypes. It is better to explore end-to-end training to create a more integrated pipeline.\n\n（2）The complete optimization process of the model is not clear, it is better to add an algorithm description.\n\n（3）Although the authors give both fully incomplete and partially incomplete multi-view clustering results, the performance of CPMN in partially incomplete multi-view clustering is poorer than the baselines. The authors should explain this result.\n\n（4）The authors should provide more qualitative results, such as t-SNE visualization, to provide deeper insights beyond quantitative metrics.\n\nLIMITATIONS:\nThe paper discusses some limitations, including the influence of the initial prototype quality on the clustering results and the assumption of the number of clusters. There is no potential negative societal impact of their work.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel framework, CPMN, to address the understudied problem of fully incomplete multi-view clustering. CPMN designs an explicit prototype matching strategy to effectively align cross-view instances through category-level correspondence. Additionally, it employs a correspondence-free graph contrastive learning approach and a prototype-aware contrastive learning mechanism to learn semantic consistency across views. The experiments are implemented on five datasets with the comparison of many methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1） The paper is straightforward and easy to understand.\n2） Relatively extensive comparisons are conducted with existing fully/partially incomplete multi-view approaches. \n\nWeaknesses\n1) Critical components of CPMN, such as prototype matching lack theoretical explanation or deeper intuitive understanding.\n2) The innovation is limited since contrastive learning is widely used in multi-view clustering.\n3) Authors should extend the parameter sensitivity analysis to more key hyperparameters, such as $k$ and $\\tau$, to demonstrate the robustness of the model.\n4) Visualization is missing. Please visualize the matched cross-view prototypes to demonstrate the learned correspondences.\n\nQUESTIONS:\n1) Critical components of CPMN, such as prototype matching lack theoretical explanation or deeper intuitive understanding.\n2) The innovation is limited since contrastive learning is widely used in multi-view clustering.\n3) Authors should extend the parameter sensitivity analysis to more key hyperparameters, such as $k$ and $\\tau$, to demonstrate the robustness of the model.\n4) Visualization is missing. Please visualize the matched cross-view prototypes to demonstrate the learned correspondences.\n\nLIMITATIONS:\nYes. The limitations have been discussed in the conclusion and other concerns can be found in weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents the CPMN (Contrastive Prototype Matching Network for multi-view clustering), designed to operate without requiring any cross-view correspondences. CPMN utilizes a prototype matching mechanism that circumvents the need for strong semantic consistency or strict view alignment assumptions. Extensive experiments conducted on various datasets demonstrate the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n(1)\tThe paper proposes a framework capable of handling fully incomplete multi-view clustering scenarios, addressing an important limitation of existing methods and broadening potential application domains.\n\n(2)\tThe proposed multi-stage architecture is intuitively structured and relatively easy to follow.\n\nWeaknesses:\n\n(1)\tThe core component prototype graph matching lacks rigorous theoretical grounding and interpretability.\n\n(2)\tAs a multi-stage training framework, early-stage clustering outcomes may adversely influence later modules such as prototype graph matching.\n\n(3)\tThe experimental evaluation includes only 100% and 50% missing view rates. Incorporating intermediate levels of incompleteness (e.g., 20%, 60%) and plotting clustering performance against the incompleteness rate would provide a more comprehensive assessment of robustness.\n\n(4)\tThe paper does not present convergence curves for loss functions or clustering metrics.\n\n(5)\tSome recent contrastive learning based clustering methods are encouraged to be reviewed, such as “Decoupled Contrastive Multi-view Clustering with High-order Random Walks” and “Incomplete Multi-view Clustering via Diffusion Contrastive Generation”.\n\n(6)\tThe ablation study lacks clarity. Components of CPMN were not strictly isolated or replaced to verify individual contributions. A more detailed description of the ablation design is needed to support the conclusions drawn.\n\nQUESTIONS:\n-\tWhat is the rationale for using Mutual Nearest Neighbors  instead of the more commonly adopted k-Nearest Neighbors in the Correspondence-Free Graph Contrastive Learning phase?\n-\tHow does its computational efficiency compare with existing methods such as SMILE?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
This work proposes a CPMN framework to address the challenging fully incomplete multi-view clustering problem where no cross-view correspondences exist. It integrates a two-stage contrastive learning approach and an explicit prototype graph matching strategy. Experimental results on five benchmark datasets demonstrate improvements over baselines.

STRENGTHS AND WEAKNESSES:
Strengths：
It provides a multi-stages approach to solve the problem of completely missing multi-view clustering. The paper is clearly and effectively written. Extensive experiments and ablation studies are conducted in the paper.

Weaknesses：
1）The authors use MNN instead of the commonly used kNN in the first stage of Correspondence-Free Graph Contrastive Learning. When dealing with large scale data, MNN takes increased time compared to kNN.
2） The efficiency of CPMN is unknown. The authors should report the training time and inference time of CPMN on different sized datasets.
3） To visualize the training process, please give the curve of the prototype matching accuracy versus the training process.

QUESTIONS:
Most of the problems have already been mentioned in Weaknesses. More implementation details can be provided for the following problems: 
1）In section3.1, the introduction of using MNN to construct positive pairs in Correspondence-Free Graph Contrastive Learning is not clear enough. There may be a failure to find MNN, so how to deal with it?
2）Why use K-Means to initialize the prototype in the first stage, while using a learnable clustering layer to optimize the prototype in the second stage?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 4
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-07-01