关键词: 多视图图聚类, 互补信息, 视图关系学习, 图学习, 图融合

Abstract: Simple and efficient multi-view graph clustering methods have received a lot of attention in recent years. Most existing multi-view graph clustering algorithms do not sufficiently mine the information hidden in multi-view data, resulting in sub-optimal clustering results. To address this problem, a multi-view graph clustering algorithm combining view relation learning and graph learning (MVG) is proposed. The method integrates graph fusion and spectral clustering learning in a unified framework based on multi-view self-expressions. View self-expression learning is extended to reveal the low-dimensional subspace distribution of high-dimensional data, jointly constrai-ning the geometric structure of the multi-view data distribution. And the complementary information between the multi-view view data is exploited to optimize the similarity graphs for each view. The spectral clustering input graphs and the weights occupied by different views are alternatively optimized. Finally, by learning the graph structure of the fusion graph, a connection with spectral clustering is established and a high-quality spectral clustering input graph is constructed. The information hidden in the multi-view data is fully explored and exploited to be highly competitive in terms of clustering performance. Experiments are conducted on five widely used multi-view datasets to verify the effectiveness and feasibility of the algorithm. The experimental data on the reuters-1200 dataset show 0.22, 0.09, 0.115, 0.152, 0.032 and 0.185 improvement over the next best method in terms of clustering evaluation metrics respectively.

Key words: multi-view graph clustering, complementary information, view relationship learning, graph learning, graph fusion