CONSISTENT MULTIVIEW CLUSTERING BASED ON BIPARTITE GRAPH MATRIX

In order to make full use of consistency information and improve clustering performance and adaptive ability, a consistent multi-view clustering method based on bipartite graph matrix is proposed. A small number of unified anchors representing different views were used to represent the consistency information. Those information were fused to form a unified graph matrix. In addition, the weight of each bipartite graph was automatically determined by determining continuous anchors in a mutually reinforcing way. Further, the variable optimization problem was solved step by step by alternating optimization. The experimental results on synthetic data sets and real data sets prove the superiority of the proposed method in clustering accuracy and adaptive ability.