1. A Generalized Robust Minimization Framework for Low-Rank Matrix Recovery.
- Author
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Wen-Ze Shao, Qi Ge, Zong-Liang Gan, Hai-Song Deng, and Hai-Bo Li
- Subjects
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GENERALIZATION , *ROBUST control , *LOW-rank matrices , *PROBLEM solving , *ERROR analysis in mathematics , *ALGORITHMS - Abstract
This paper considers the problem of recovering low-rank matrices which are heavily corrupted by outliers or large errors. To improve the robustness of existing recovery methods, the problem is solved by formulating it as a generalized non-smooth non-convex minimization functional via exploiting the Schatten p-norm (0 < p ⩽ 1) and Lq(0 < q ⩽ 1) seminorm. Two numerical algorithms are provided based on the augmented Lagrange multiplier (ALM) and accelerated proximal gradient (APG) methods as well as efficient root-finder strategies. Experimental results demonstrate that the proposed generalized approach is more inclusive and effective compared with state-of-the-art methods, either convex or nonconvex. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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