Multi-view clustering via dynamic unified bipartite graph learning

Abstract

Multi-view clustering algorithms based on graph learning have the ability to extract the potential association between data samples, which has been a concern of many researchers in recent years. However, existing algorithms have two limitations: (1) they directly learn from the raw graph, which includes noise and outliers, and they construct the graph filter statically, biasing the clustering results; (2) during graph construction, they mainly use the information of a single structure and fail to fully extract the multi-granular structural information among the data. To address these issues, this paper proposes a novel multi-view clustering method via dynamic unified bipartite graph learning. Specifically, a learnable graph filter is first refined to dynamically filter the original data feature space, gradually filtering out the undesirable high-frequency noise and achieving a clustering-friendly smooth representation. Second, a unified bipartite graph is constructed by combining the multi-granular structural information of different views to better explore the distinct and common information of each view. In one framework, the dynamic filter and multi-granular structure information are combined to iteratively learn the unified bipartite graph. An efficient iterative algorithm is designed to decompose the objective function into small-scale subproblems for solving. Extensive experiments on benchmark datasets show the superiority of the proposed algorithm over several existing state-of-the-art multi-view clustering algorithms.