Contrastive and attentive graph learning for multi-view clustering

Wang, Ru and Li, Lin and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Wang, Peipei and Liu, Peiyu (2022) Contrastive and attentive graph learning for multi-view clustering. Information Processing and Management, 59 (4):102967. pp. 1-14. ISSN 0306-4573


Abstract

Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies generate fusion graphs and constrain multi-view consistency by clustering loss. We argue that local pair-view consistency can achieve fine-modeling of consensus information in multiple views. Towards this end, we propose a novel Contrastive and Attentive Graph Learning framework for multi-view clustering (CAGL). Specifically, we design a contrastive fine-modeling in multi-view graph learning using maximizing the similarity of pair-view to guarantee the consistency of multiple views. Meanwhile, an Att-weighted refined fusion graph module based on attention networks to capture the capacity difference of different views dynamically and further facilitate the mutual reinforcement of single view and fusion view. Besides, our CAGL can learn a specialized representation for clustering via a self-training clustering module. Finally, we develop a joint optimization objective to balance every module and iteratively optimize the proposed CAGL in the framework of graph encoder–decoder. Experimental results on six benchmarks across different modalities and sizes demonstrate that our CAGL outperforms state-of-the-art baselines.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 12 May 2022 23:13
Last Modified: 12 May 2022 23:13
Uncontrolled Keywords: Graph learning, Contrastive learning, Attention networks, Multi-view clustering
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460506 Graph, social and multimedia data
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Identification Number or DOI: https://doi.org/10.1016/j.ipm.2022.102967
URI: http://eprints.usq.edu.au/id/eprint/48462

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