Semi-supervised multi-view clustering based on constrained nonnegative matrix factorization

Most existing clustering approaches address multi-view clustering problems by graph regularized nonnegative matrix factorization to obtain the new representation of each view separately. And then, the additional regularization term is introduced to fuse these representations. However, the graph regularization is no guarantee that the representation obtained by mapping data points has the consistent label information with the original data. In addition, we may have a number of labeled samples in practice. This paper proposes a new semi-supervised multi-view clustering method based on Constrained Nonnegative Matrix Factorization with sparseness constraint, which is called MVCNMF. Our method first learns the representation by introducing an auxiliary matrix for each view and constructs the label constraint matrix shared among all views, where the label constraint matrix can guarantee that the label information is fused into the new representation of each view. Then, by applying the co-regularization and the sparseness constraint in auxiliary matrices, the complementary information from different views is integrated, and the robust feature of each view is extracted. Further, we also present the convergence proof and the computational complexity analysis of our method. Finally, extensive experiments on real datasets have demonstrated that our proposed approach can achieve a better clustering quality as compared to state-of-the-art multi-view clustering methods. (C) 2019 Elsevier B.V. All rights reserved.