Academic Journal
Deep multi-view clustering based on instance-level adaptive structural contrastive learning.
| Τίτλος: | Deep multi-view clustering based on instance-level adaptive structural contrastive learning. |
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| Συγγραφείς: | Yang Z; School of Artificial Intelligence and Automation, National Key Laboratory of Multispectral Information Intelligent Processing Technology, Huazhong University of Science and Technology, Wuhan, 430074, China., Tan Y; School of Artificial Intelligence and Automation, National Key Laboratory of Multispectral Information Intelligent Processing Technology, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address: yhtan@hust.edu.cn. |
| Πηγή: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Jun; Vol. 198, pp. 108581. Date of Electronic Publication: 2026 Jan 09. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York : Pergamon Press, [c1988- |
| Ιατρικοί όροι (MeSH): | Deep Learning* , Neural Networks, Computer*, Clustering Algorithms ; Cluster Analysis ; Humans ; Algorithms |
| Περίληψη: | Deep multi-view clustering has rapidly developed in recent years, leveraging the powerful representation capabilities of deep neural networks. Among them, instance-level feature contrastive learning is widely used in deep multi-view clustering to align the embedded features of the same samples across views. However, it overlooks the constraint on the clustering structure consistency across views. Drawing inspiration from the instance-level feature contrastive learning mentioned above, we propose deep multi-view clustering based on instance-level adaptive structural contrastive learning. First, considering that different views may have varying impacts on the clustering results of different multi-view samples, we utilize Transformer Pooling module to adaptively fuse different views of different samples, obtaining the fused view. The final clustering results are derived from the fused view as well. Secondly, we propose a clue-consistency-based approach to identify sample pairs that exhibit consistent clustering structures between each view and the fused view, forming local and global consistency clustering information. Incorporating global consistency clustering information, we construct adjacency matrices for each view and the fused view. Since adjacency matrices record the clustering structure of each sample with others, we propose the instance-level adaptive structural contrastive learning, leveraging the above local consistency information to align the overall clustering structures of the same samples across different views and the fused view. By comparing the results of proposed method with several state-of-the-art methods on multiple multi-view datasets, we demonstrate the superiority of the proposed approach. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Clustering structure consistency; Contrastive learning; Multi-view clustering; Multi-view fusion |
| Entry Date(s): | Date Created: 20260117 Date Completed: 20260626 Latest Revision: 20260626 |
| Update Code: | 20260626 |
| DOI: | 10.1016/j.neunet.2026.108581 |
| PMID: | 41547126 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2026.108581 |