Efficient dimension reduction algorithm via L2, 1 norm PCA
一种基于L2,1范数的PCA维数约简算法

Traditional PCA is sensitive to outliers and feature noises, PCA based on L2, 1-norm can improve the problems. Whereas present L2, 1-PCA algorithms implement dimension reduction on the rank of the matrix and the rank is complex problem. In order to solve this problem, this paper proposed using trace norm instead of rank, then the calculation of L2, 1-PCA algorithm could simplify and the efficiency could improve. It also put forward an efficient augmented Lagrange multiplierALMalgorithm for the solutions. Extensive experiments on extended Yale B face data sets verify the efficiency of the proposed algorithm.