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A Generalized Robust Minimization Framework for Low-Rank Matrix Recovery
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2014 (English)In: Mathematical problems in engineering (Print), ISSN 1024-123X, E-ISSN 1563-5147, p. 656074-Article in journal (Refereed) Published
Abstract [en]

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 p-norm (0 < p <= 1) and L-q(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.

Place, publisher, year, edition, pages
2014. p. 656074-
Keywords [en]
Thresholding Algorithm
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Other Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-146584DOI: 10.1155/2014/656074ISI: 000336137200001Scopus ID: 2-s2.0-84901775196OAI: oai:DiVA.org:kth-146584DiVA, id: diva2:724662
Note

QC 20150701

Available from: 2014-06-13 Created: 2014-06-12 Last updated: 2017-12-05Bibliographically approved

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Li, Haibo

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