WEIGHTED GRAPH EMBEDDED LOW-RANK PROJECTION LEARNING FOR FEATURE EXTRACTION

Low-rank based methods have been widely adopted to structure preserving, when the projection matrix is learned for feature extraction. However, some dilemmas still exist that degrade the classification performance: 1) The local structure of the data is ignored; 2) the reconstructed data is not consistent with the original data. To solve those problems, in this paper a weighted graph embedded low-rank projection (WGE_LRP) method is proposed. In WGE_LRP, a novel weighted graph regularization term is proposed, which can learn the local structure of the data based on the similarity of different samples. Meanwhile, an extra global information term is introduced to keep the reconstructed data consistent with the original data. Experimental results show that the proposed method can obtain competitive performance in comparison to the state-of-the-arts.