Auto-weighted multi-view clustering via hierarchical bipartite graph

Abstract

The expensive time consumption and hyper-parameters are two main drawbacks of most existing multi-view graph clustering methods. Especially in large-scale data clustering, these two defects are more serious. Aiming at these two problems, we propose a novel auto-weighted multi-view clustering method based on the hierarchical bipartite graph to effectively address these two limitations. Similar to the idea of an anchor graph, firstly, the bisecting k-means method is used instead of traditional method to generate a hierarchical anchor points set. And then, the hierarchical bipartite graph can be constructed between the original points and the anchor points of last layer. Since we only consider the anchor points on the last layer rather than using a larger number of anchors to obtain the bipartite graph, our proposed method will greatly reduce the time consumption. Moreover, the automatic learning strategy was adopted to select the appropriate weights for each view in our proposed approach, which remove the weight-related hyper-parameters and effectively avoid the vexing problem of hyper-parameters in multi-view graph clustering. Finally, experiments were carried out on synthetic data and different sizes benchmark data respectively to verify the validity of our method.