Hybrid Matrix Factorization for Multi-view Clustering

Abstract

Multi-view clustering (MVC) has gained considerable attention recently. In this paper, we present a hybrid matrix factorization (HMF) framework which is a combination of the nonnegative factorization and the symmetric nonnegative matrix factorization for MVC. HMF can uncover linear and nonlinear manifold within multi-view dataset. In addition, HMF also learns weights for each view to characterize the contribution of each view to the final common clustering assignment. The proposed model can be solved by nonnegative least squares. Unlike previous approaches, our approach can obtain the clustering results straightforwardly due to the nonnegative constraints. We conduct experiments on multi-view benchmark datasets to verify the effectiveness of our proposed approach.