A Generalized Robust Minimization Framework for Low-Rank Matrix Recovery

Wen-Ze Shao, Qi Ge, Zong-Liang Gan, Hai-Song Deng and Hai-Bo Li

Mathematical Problems in Engineering, 2014, vol. 2014, 1-8

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 nonsmooth nonconvex minimization functional via exploiting the Schatten -norm and 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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:656074

DOI: 10.1155/2014/656074

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