摘要
In this paper, a unified matrix recovery model was proposed for diverse corrupted matrices. Resulting from the separable structure of the proposed model, the convex optimization problem can be solved efficiently by adopting an inexact augmented Lagrange multiplier (IALM) method. Additionally, a random projection accelerated technique (IALM+RP) was adopted to improve the success rate. From the preliminary numerical comparisons, it was indicated that for the standard robust principal component analysis (PCA) problem, IALM+RP was at least two to six times faster than IALM with an insignificant reduction in accuracy; and for the outlier pursuit (OP) problem, IALM+RP was at least 6.9 times faster, even up to 8.3 times faster when the size of matrix was 2 000×2 000.
In this paper, a unified matrix recovery model was proposed for diverse corrupted matrices. Resulting from the separable structure of the proposed model, the convex optimization problem can be solved efficiently by adopting an inexact augmented Lagrange multiplier （IALM） method. Additionally, a random projection accelerated technique （IALM＋RP） was adopted to improve the success rate. From the preliminary numerical comparisons, it was indicated that for the standard robust principal component analysis （PCA） problem, IALM＋RP was at least two to six times faster than IALM with an insignificant reduction in accuracy; and for the outlier pursuit （OP） problem, IALM＋RP was at least 6.9 times faster, even up to 8.3 times faster when the size of matrix was 2 000×2 000.
基金
Supported by National Natural Science Foundation of China (No.51275348)
College Students Innovation and Entrepreneurship Training Program of Tianjin University (No.201210056339)
