Graph Proxy Fusion: Consensus Graph Intermediated Multi-View Local Information Fusion Clustering

Abstract

Multi-view clustering (MVC) can fuse the information of multiple views for robust clustering result, among it two fusion strategies, early-fusion and late-fusion are widely adopted. Although they have derived many MVC methods, there are still two crucial questions: (1) early-fusion forces multiple views to share a consensus latent representation, which compounds the challenge of excavating view-specific diverse local information and (2) late-fusion generates view-partitions independently and then integrates them in the following clustering procedure, where the two procedures cannot guide each other and lack necessary negotiation. In view of this, we propose a novel Graph Proxy Fusion (GPF) method to preserve and fuse view-specific local information concertedly in one unified framework. Specifically, we first propose anchor-based local information learning to capture view-specific local structural information in bipartite graphs; meanwhile, a view-consensus graph learned through self-expressiveness-based proxy graph learning module is deemed as a higher-order proxy; following, the novel graph proxy fusion module integrally embeds all lower-order bipartite graphs in the higher-order proxy via higher-order correlation theory. As a novel fusion strategy, the proposed GPF efficiently investigates the valuable consensus and diverse information of multiple views. Experiments on various multi-view datasets demonstrate the superiority of our method.