Deep Contrastive Multi-View Subspace Clustering With Representation and Cluster Interactive Learning

Abstract

Multi-view clustering is an important approach to mining the valuable information within multi-view data. In this paper, we propose a novel multi-view deep subspace clustering method based on contrastive learning and Cauchy-Schwarz (CS) divergence. Our method not only uses contrastive learning techniques and block diagonalization constraints to guide representation matrix learning, but also combines representation learning and clustering processes to achieve the interaction of representation and clustering. First, we introduce a novel loss function based on CS divergence in the clustering module to achieve the interaction of representation and clustering. Second, we propose an extension of the multiple positive and negative pair diffusion method to enhance contrastive learning. Finally, we establish the equivalence between contrastive clustering and spectral clustering with orthogonal constraints, leading to a comprehensive model optimization. We evaluate our method on six publicly available datasets and compare its performance with eight competing methods. The results demonstrate the superiority of our method over the compared multi-view clustering methods.