Learning low-rank graph with enhanced supervision

Abstract

In this paper, we propose a new semi-supervised graph construction method, which is capable of adaptively learning the similarity relationship between data samples by fully exploiting the potential of pairwise constraints, a kind of weakly supervisory information. Specifically, to adaptively learn the similarity relationship, we linearly approximate each sample with others under the regularization of the low-rankness of the matrix formed by the approximation coefficient vectors of all the samples. In the meanwhile, by taking advantage of the underlying local geometric structure of data samples that is empirically obtained, we enhance the dissimilarity information of the available pairwise constraints via propagation. We seamlessly combine the two adversarial learning processes to achieve mutual guidance. We cast our method as a constrained optimization problem and provide an efficient alternating iterative algorithm to solve it. Experimental results on five commonly-used benchmark datasets demonstrate that our method produces much higher classification accuracy than state-of-the-art methods, while running faster.