Bipartite Graph-based Discriminative Feature Learning for Multi-View Clustering

Abstract

Multi-view clustering is an important technique in machine learning research. Existing methods have improved in clustering performance, most of them learn graph structure depending on all samples, which are high complexity. Bipartite graph-based multi-view clustering can obtain clustering result by establishing the relationship between the sample points and small anchor points, which improve the efficiency of clustering. Most bipartite graph-based clustering methods only focus on topological graph structure learning depending on sample nodes, ignore the influence of node features. In this paper, we propose bipartite graph-based discriminative feature learning for multi-view clustering, which combines bipartite graph learning and discriminative feature learning to a unified framework. Specifically, the bipartite graph learning is proposed via multi-view subspace representation with manifold regularization terms. Meanwhile, our feature learning utilizes data pseudo-labels obtained by fused bipartite graph to seek projection direction, which make the same label be closer and make data points with different labels be far away from each other. At last, the proposed manifold regularization terms establish the relationship between constructed bipartite graph and new data representation. By leveraging the interactions between structure learning and discriminative feature learning, we are able to select more informative features and capture more accurate structure of data for clustering. Extensive experimental results on different scale datasets demonstrate our method achieves better or comparable clustering performance than the results of state-of-the-art methods.