Deep Constrained Low-Rank Subspace Learning for Multi-View Semi-Supervised Classification

Semi-supervised classification receives increasing interests because it can predict class labels based on both limited labeled and sufficient unlabeled data. In this letter, we propose a deep constrained low-rank subspace learning (DCLSL) method for multi-view semi-supervised classification. Specifically, we integrate deep constrained matrix factorization, low-rank subspace learning, and class label learning into a unified objective function to jointly learn data similarity matrices and class label matrix. DCLSL is able to obtain the discriminative subspace representation of each view and effectively aggregate similarity matrices of multiple views, resulting in better classification performance. Experimental results on various datasets demonstrate the effectiveness of our method.