Semi-supervised multi-view clustering with Graph-regularized Partially Shared Non-negative Matrix Factorization

Non-negative matrix factorization is widely used in multi-view clustering due to its ability of learning a common dimension-reduced factor. Recently, it is combined with the label information to improve the clustering, but the affection of the dimension-reduction to the classes of the labeled data is seldom considered. Motivated by that the graph constraint can keep the geometric structure of the data, it is employed to restrict the class variation of the data caused by the dimension reduction, and a semi-supervised method called Graph-regularized Partially Shared Non-negative Matrix Factorization (GPSNMF) is proposed for multi-view clustering in this paper. In our method, the affinity graph of each view is constructed to encode the geometric information, and the corresponding multiplication update algorithm based on alternative iteration rule is derived. In the experiments, two clustering approaches are tested based on the results of the proposed GPSNMF, and four real-world databases with different label proportions are performed to demonstrate the advantages of our method over the state-of-the-art methods.