Purely Contrastive Multiview Subspace Clustering

Multiview subspace clustering (MVSC) aims to integrate complementary information from different views to accurately reveal the subspace structure of a multiview dataset. Traditional MVSC methods often emphasize the aggregation of samples within the same subspace, while neglecting the separation of samples across different subspaces. In this article, we incorporate contrastive learning techniques into the MVSC framework, developing a contrastive data self-representation module, a contrastive regularizer for the reconstruction coefficient matrix in each view, and a contrastive alignment term to obtain a consensus coefficient matrix that fuses structural information from the reconstruction coefficient matrices. This leads to the framework of a purely contrastive MVSC (PCMVSC) approach. We elaborate on the superiority of the proposed modules in PCMVSC over similar ones in existing methods and show that the consensus reconstruction coefficient matrix obtained by PCMVSC can effectively uncover the underlying subspace structure of multiview datasets. Extensive subspace clustering experiments prove the effectiveness of PCMVSC and reveal that it outperforms various existing multiview clustering algorithms.