关键词: 图像处理, 特征提取, 低秩表示, 人脸识别

Abstract:

The feature extraction algorithms only use the local structure or the global structure of data, so they will not get the global optimal projection matrix, and projection matrix does not have good interpretability. In this paper, a low-rank projection learning algorithm based on neighborhood graph is proposed. The algorithm imposes the graph constraint on the reconstruction error of data to maintain the local structure of data , and introduces a low-rank term to preserve the global structure; the property of L2，1 norm row sparsity is used to constrain the projection matrix. In this way, redundant features can be eliminated, and the interpretability of projection matrix can be improved. Meanwhile, a noise sparse term is introduced to weaken the interference of noise from samples. The model is solved by alternating iteration method, and the experimental results on multiple datasets show that the algorithm can effectively improve the classification accuracies.

Key words: image processing, feature extraction, low-rank representation, face recognition