Search

Semi-supervised multi-view learning methods aim to boost the learning performance by conjunction with labeled data, because the label information can enhance the discriminant ability of the learned model. Recently, non-negative matrix factorization has received widespread attention in semi-supervised multi-view learning due to its powerful ability of feature extraction. However, the problem of very limited available labeled data is seldom considered. In this case, it is hard for existing approaches to obtain satisfied performance. Motivated by that the label propagation can classify a large number of unlabeled data with few labeled data, in this paper, we propose a novel semi-supervised multi-view learning approach to address the problem of sparse labeled data, called Label Propagation based Non-negative Matrix Factorization (LPNMF). In our model, the intrinsic manifold structure of data is constructed by the adaptive neighbors technology. Based on this intrinsic manifold structure, the label propagation is further employed to make full use of the limited labeled data. Besides, we design an efficient alternating algorithm for solving the optimization problem and provide theoretical analysis on its convergence properties and computational complexity. Finally, experiments on four real-world datasets demonstrate the advantage of our proposed methods, with comparison to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]