Diversity-Induced Bipartite Graph Fusion for Multiview Graph Clustering

Multi-view graph clustering can divide similar objects into the same category through learning the relationship among samples. To improve clustering efficiency, instead of all sample-based graph learning, the bipartite graph learning method can achieve efficient clustering by establishing the graph between data points and a few anchors, so it becomes an important research topic. However, most these bipartite graph-based multi-view clustering approaches focused on consistent information learning among views, ignored the diversity information of each view, which is not conductive to improve clustering precision. To address this issue, a diversity-induced bipartite graph fusion for multiview graph clustering (DiBGF-MGC) is proposed to simultaneously consider the consistency and diversity of multiple views. In our method, the constraint of diversity is achieved via minimizing the diversity of each view and minimizing the inconsistency of diversity in different views. The former ensures the sparse of diversity information, and the later ensures the diversity information is private information of each view. Specifically, we separate the bipartite graph to the consistent part and the divergent part in order to remove the diversity parts while preserving the consistency among multiple views. The consistent parts are used to learn the consensus bipartite graph, which can obtain a clear clustering structure due to eliminating diversity part from original bipartite graph. The diversity part is formulated by intra-view constraint and inter-views inconsistent constraint, which can better distinguish diversity part from original bipartite graph. The consistent learning and diversity learning can be improved iteratively via leveraging the results of the other one. Experiment shows that the proposed DiBGF-MGC method obtains better clustering results than state-of-the-art methods on several benchmark datasets.