Collaborative PLSA for multi-view clustering

Data has multi-view representations from various feature spaces in real world. Multi-view clustering algorithms allow leveraging information from multiple views of the data and this may substantially improve the clustering result obtained by using a single view. In this paper, we propose a novel algorithm called Collaborative PLSA (C-PLSA) for multi-view clustering, which works on the assumption that the clustering from one view should agree with the clustering from another view. The proposed C-PLSA combines individual PLSA models on two different views, and imports a regularizer to force the both clustering results agree across the two views. To solve the regularized problem, an alternative optimization algorithm based on generalized EM (GEM) is adopted for maximum likelihood estimation. Experiments on two real-world datasets, i.e., Reuters multilingual text and Corel images, demonstrate the improved performance of our proposed method over some related work.