Semi-supervised multi-view learning by using label propagation based non-negative matrix factorization

摘要

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.