Graph Regularized Low-Rank Representation for Semi-Supervised Learning

Abstract

Due to the excellent performance in exploring the structure of low-dimensional subspaces, the low-rank representation (LRR) has recently attracted wide attention of the researchers. However, in most current semi-supervised learning problems based on LRR method, the two steps of graph construction and semi-supervised learning are separated. Therefore, the existing label information cannot be well used to guide the construction of the affinity graph. Thus, these methods cannot guarantee the results are the global optimal solutions. In this paper, we propose a graph regularized low-rank representation for semi-supervised learning, termed as GLRSC. Combing the construction of the affinity graph and the semi-supervised learning, and solving the joint optimization, the proposed GLRSC method can get the global optimal solution. The experimental results on some benchmark datasets show that the effectiveness of the proposed GLRSC method.