Bidirectional Probabilistic Multi-Graph Learning and Decomposition for Multi-View Clustering

Xinxin Wang, Yongshan Zhang, Yicong Zhou

Abstract: Graph-based multi-view clustering has attracted remarkable attention due to its impressive performance. However, the typical framework consisting of graph learning and indicator generation may fail to align learned graphs with the underlying data structure due to the unidirectional pipeline from refined graphs to indicator generation. Another common problem is the inadequate prior information in graph learning methods. This paper proposes a Bidirectional Probabilistic Multi-graph Learning and Decomposition (BPMLD) method by establishing an explicit bidirectional pipeline between graph learning and indicator generation for multi-view clustering. Specifically, we design a confidence term based on clustering probability indicators and fuse it with graph learning to form clustering confidence driven graph learning. Meanwhile, graph tensor learning is introduced to recover the high-order correlations among the refined graphs. We further propose a multi-graph probability decomposition module to adaptively produce cluster indicators with probability representation from the refined graphs. The seamless integration between graph learning and indicator generation enables them to interact directly and enhance each other. To solve the proposed model, we design an effective optimization algorithm. Extensive experiments demonstrate the effectiveness of our method compared to state-of-the-art methods. The code is available at: https://github.com/W-Xinxin/BPMLD.