{"title":"Deep Multi-View Contrastive Clustering via Graph Structure Awareness","authors":"Lunke Fei;Junlin He;Qi Zhu;Shuping Zhao;Jie Wen;Yong Xu","doi":"10.1109/TIP.2025.3573501","DOIUrl":null,"url":null,"abstract":"Multi-view clustering (MVC) aims to exploit the latent relationships between heterogeneous samples in an unsupervised manner, which has served as a fundamental task in the unsupervised learning community and has drawn widespread attention. In this work, we propose a new deep multi-view contrastive clustering method via graph structure awareness (DMvCGSA) by conducting both instance-level and cluster-level contrastive learning to exploit the collaborative representations of multi-view samples. Unlike most existing deep multi-view clustering methods, which usually extract only the attribute features for multi-view representation, we first exploit the view-specific features while preserving the latent structural information between multi-view data via a GCN-embedded autoencoder, and further develop a similarity-guided instance-level contrastive learning scheme to make the view-specific features discriminative. Moreover, unlike existing methods that separately explore common information, which may not contribute to the clustering task, we employ cluster-level contrastive learning to explore the clustering-beneficial consistency information directly, resulting in improved and reliable performance for the final multi-view clustering task. Extensive experimental results on twelve benchmark datasets clearly demonstrate the encouraging effectiveness of the proposed method compared with the state-of-the-art models.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"34 ","pages":"3805-3816"},"PeriodicalIF":13.7000,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11021328/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Multi-view clustering (MVC) aims to exploit the latent relationships between heterogeneous samples in an unsupervised manner, which has served as a fundamental task in the unsupervised learning community and has drawn widespread attention. In this work, we propose a new deep multi-view contrastive clustering method via graph structure awareness (DMvCGSA) by conducting both instance-level and cluster-level contrastive learning to exploit the collaborative representations of multi-view samples. Unlike most existing deep multi-view clustering methods, which usually extract only the attribute features for multi-view representation, we first exploit the view-specific features while preserving the latent structural information between multi-view data via a GCN-embedded autoencoder, and further develop a similarity-guided instance-level contrastive learning scheme to make the view-specific features discriminative. Moreover, unlike existing methods that separately explore common information, which may not contribute to the clustering task, we employ cluster-level contrastive learning to explore the clustering-beneficial consistency information directly, resulting in improved and reliable performance for the final multi-view clustering task. Extensive experimental results on twelve benchmark datasets clearly demonstrate the encouraging effectiveness of the proposed method compared with the state-of-the-art models.