Graph Contrastive Partial Multi-View Clustering

Wang, Yiming; Chang, Dongxia; Fu, Zhiyuan; Wen, Jiangtao; Zhao, Yao

doi:10.1109/tmm.2022.3210376

Cited by 12 publications

(2 citation statements)

References 40 publications

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“…(3) GCNs-based methods. GCNs-based methods explore common representations of multiple views through structural information among them [33,34]. (4) Contrastive-based methods.…”

Section: Deep Incomplete Multi-view Clusteringmentioning

confidence: 99%

Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment

Jin¹,

Wang²,

Dong³

et al. 2023

Preprint

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The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC). Although several attempts have been proposed to address IMVC, they suffer from the following drawbacks: i) Existing methods mainly adopt cross-view contrastive learning forcing the representations of each sample across views to be exactly the same, which might ignore view discrepancy and flexibility in representations; ii) Due to the absence of non-observed samples across multiple views, the obtained prototypes of clusters might be unaligned and biased, leading to incorrect fusion. To address the above issues, we propose a Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering. Firstly, unlike existing contrastive-based methods, we adopt pair-observed data alignment as 'proxy supervised signals' to guide instance-to-instance correspondence construction among views. Then, regarding of the shifted prototypes in IMVC, we further propose a prototype alignment module to achieve incomplete distribution calibration across views. Extensive experimental results showcase the effectiveness of our proposed modules, attaining noteworthy performance improvements when compared to existing IMVC competitors on benchmark datasets.

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“…(3) GCNs-based methods. GCNs-based methods explore common representations of multiple views through structural information among them [33,34]. (4) Contrastive-based methods.…”

Section: Deep Incomplete Multi-view Clusteringmentioning

confidence: 99%

Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment

Jin¹,

Wang²,

Dong³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Traditional machine learning approaches can be broadly classified into four categories: subspace learning methods [ 12 ], non-negative matrix factorization (NMF) methods [ 13 , 14 ], graph-based techniques [ 1 , 15 ], and a range of kernel-based methods [ 16 ]. However, traditional shallow models often struggle to effectively learn feature representations from large datasets [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

View-Driven Multi-View Clustering via Contrastive Double-Learning

Liu,

Zhu,

et al. 2024

Entropy

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Multi-view clustering requires simultaneous attention to both consistency and the diversity of information between views. Deep learning techniques have shown impressive abilities to learn complex features when working with extensive datasets; however, existing deep multi-view clustering methods often focus only on either consistency information or diversity information, making it difficult to balance both aspects. Therefore, this paper proposes a view-driven multi-view clustering using the contrastive double-learning method (VMC-CD), aiming to generate better clustering results. This method first adopts a view-driven approach to consider information from other views to encourage diversity, thus guiding feature learning. Additionally, it presents the idea of dual contrastive learning to enhance the alignment of views at both the clustering and feature levels. The VMC-CD method’s superiority over various cutting-edge methods is substantiated by experimental findings across three datasets, affirming its effectiveness.

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