Low-Rank Projection Learning via Graph Embedding

Abstract

With robustness to various corruptions, it is the local geometrical relationship among data that plays an important role in the recognition/clustering task of subspace learning (SL). However, a lot of previous SL methods cannot take into consideration both of the local neighborhood and the robustness, which results in poor performance in image classification and feature extraction. In this paper, a robust SL method is proposed to solve the feature extraction problem, named as Low-Rank Projection Learning via Graph Embedding (LRP-GE). The proposed algorithm enjoys two merits. First, it preserves the local neighborhood information among data by introducing the graph embedding (GE). Second, it alleviates the impact of noise and corruption by learning a robust subspace based on the low-rank projection. We cast the problem as a convex optimization problem and provide an iterative solution that can be solved efficiently in polynomial time. Extensive experiments performed on four benchmark data sets demonstrate that the proposed method performs favorably against other well-established SL methods in image classification.