A Multi-View Co-Training Clustering Algorithm Based on Global and Local Structure Preserving

Abstract

Multi-view clustering which integrates the complementary information from different views for better clustering, is a fundamental and important topic in machine learning. In this paper, we present a multi-view co-training clustering algorithm based on global and local structure preserving. Here the global structure is referred to the integration of the within-cluster compactness and between-cluster separation; the local structure is referred to the neighborhood information. Our algorithm at first preserves both the global and local structure to the subspace in each view. And then, this algorithm obtains the clustering result in the subspace of each view, and utilizes the clustering labels of one view to guide the subspace clustering in another view. In this way, the differences and compatibilities among the multiple views are fused together to form the final cluster partition. Therefore, the clustering result takes full account of the global and local structure information of the multi-view data, which is helpful to improve the of clustering accuracy. Experimental results on the multi-view text datasets and image datasets demonstrate the effectiveness and correctness of the proposed algorithm.