A Generalized Robust Minimization Framework for Low-Rank Matrix Recovery
2014 (English)In: Mathematical problems in engineering (Print), ISSN 1024-123X, E-ISSN 1563-5147, 656074- p.Article in journal (Refereed) Published
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. 656074- p.
IdentifiersURN: urn:nbn:se:kth:diva-146584DOI: 10.1155/2014/656074ISI: 000336137200001ScopusID: 2-s2.0-84901775196OAI: oai:DiVA.org:kth-146584DiVA: diva2:724662
QC 201507012014-06-132014-06-122015-07-01Bibliographically approved