Graph regularized non-negative local coordinate factorization with pairwise constraints for image representation

Chen et al. proposed a non-negative local coordinate factorization algorithm for feature extraction (NLCF) [1], which incorporated the local coordinate constraint into non-negative matrix factorization (NMF). However, NLCF is actually a unsupervised method without making use of prior information of problems in hand. In this paper, we propose a novel graph regularized non-negative local coordinate factorization with pairwise constraints algorithm (PCGNLCF) for image representation. PCGNLCF incorporates pairwise constraints and graph Laplacian into NLCF. More specifically, we expect that data points having pairwise must-link constraints will have the similar coordinates as much as possible, while data points with pairwise cannot-link constraints will have distinct coordinates as much as possible. Experimental results show the effectiveness of our proposed method in comparison to the state-of-the-art algorithms on several real-world applications.