A Semi-supervised Learning Algorithm Based on Low Rank and Weighted Sparse Graph for Face Recognition

Traditional graph-based semi-supervised learning can not capture both the global and local structures of the data exactly. In this paper, we propose a novel low rank and weighted sparse graph. First, we utilize exact low rank representation by the nuclear norm and Forbenius norm to capture the global subspace structure. Meanwhile, we build the weighted sparse regularization term with shape interaction information to capture the local linear structure. Then, we employ the linearized alternating direction method with adaptive penalty to solve the objective function. Finally, the graph is constructed by an effective post-processing method. We evaluate the proposed method by performing semi-supervised classification experiments on ORL, Extended Yale B and AR face database. The experimental results show that our approach improves the accuracy of semi-supervised learning and achieves the state-of-the-art performance.