Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm

The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal processing. However, solving the nuclear norm-based relaxed convex problem usually leads to a suboptimal solution of the o...

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Publié dans:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 25(2016), 2 vom: 10. Feb., Seite 829-39
Auteur principal:	Lu, Canyi (Auteur)
Autres auteurs:	Tang, Jinhui, Yan, Shuicheng, Lin, Zhouchen
Format:	Article en ligne
Langue:	English
Publié:	2016
Accès à la collection:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Sujets:	Journal Article Research Support, Non-U.S. Gov't