Underlying-Complementarity and Surrounding-Correspondence for Multi-View Clustering

In this paper, we study the intrinsic connections between informative representation among different views and sufficient information among reconstructed views in multi-view clustering. To this end, we propose a novel method called underlying-complementarity and surrounding-correspondence for multi-view clustering (CCMC) including two goals: 1) The surrounding-correspondence is learned by domain correspondence of surrounding data points based on decoder regularization to capture the supplement structure information. 2) The underlying-complementarity is learned by pseudo-class-label with allocation matrix where the contrastive learning is applied to obtain the supervised information and the pyramid network auto-fuses multi-view information which is based on different latent layers of the different views. Compared to existing works, the advantages of the proposed method lie in enhancing the combinative as well as deep usage of complementary and domain correspondence information for better performance of clustering. CCMC is capable of guiding the network learning without intact and correct corresponding multi-view data. Extensive experiments demonstrate the promising performance even in the case of missing and unaligned data compared with state-of-the-art approaches on the multi-view clustering task.