Multi-View Clustering via Eliminating the Complementarity Controversy

Multi-view clustering aims to use heterogeneous information from different views to discover the underlying data structure and divide the samples into clusters. Consistency and complementarity are two key elements that affect the performance of multi-view clustering. Consistency emphasizes the semantic similarity between different views. Complementarity, on the other hand, emphasizes the mutual supplementation of specific information within each view. At present, the study of consistency has been relatively in-depth, but the study of complementarity is controversial, in which some methods believe that consistency and complementarity can assist each other, but constraining them to the same feature space actually causes a conflict between them. Other approaches accordingly argue that complementary information should be discarded, but this would result in a waste of information. Intuitively, complementarity should exist. The contribution of this paper is to find that existing methods do not have enough insight into the essence of complementarity, i.e., consistency and complementarity are not independent but entangled with each other, which results in conflict. Motivated by this finding, this paper realizes the separation of the two kinds of information through disentangling, specifically making them located in different feature subspaces instead of the same feature space, thus developing a multi-view clustering algorithm that takes into account both consistency and complementarity, effectively extracting the complementary information while avoiding the conflict between consistency and complementarity. Comparative experiments on standard datasets demonstrate the effectiveness of the proposed algorithm.