Deep Multiview Clustering by Contrasting Cluster Assignments

• URL: http://arxiv.org/abs/2304.10769v4
• Date: Thu, 10 Aug 2023 14:46:15 GMT
• Title: Deep Multiview Clustering by Contrasting Cluster Assignments
• Authors: Jie Chen, Hua Mao, Wai Lok Woo, and Xi Peng
• Abstract summary: Multiview clustering aims to reveal the underlying structure of multiview data by categorizing data samples into clusters. We propose a cross-view contrastive learning (C) method that learns view-invariant representations and produces clustering results by contrasting the cluster assignments among multiple views.
• Score: 14.767319805995543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiview clustering (MVC) aims to reveal the underlying structure of multiview data by categorizing data samples into clusters. Deep learning-based methods exhibit strong feature learning capabilities on large-scale datasets. For most existing deep MVC methods, exploring the invariant representations of multiple views is still an intractable problem. In this paper, we propose a cross-view contrastive learning (CVCL) method that learns view-invariant representations and produces clustering results by contrasting the cluster assignments among multiple views. Specifically, we first employ deep autoencoders to extract view-dependent features in the pretraining stage. Then, a cluster-level CVCL strategy is presented to explore consistent semantic label information among the multiple views in the fine-tuning stage. Thus, the proposed CVCL method is able to produce more discriminative cluster assignments by virtue of this learning strategy. Moreover, we provide a theoretical analysis of soft cluster assignment alignment. Extensive experimental results obtained on several datasets demonstrate that the proposed CVCL method outperforms several state-of-the-art approaches.

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