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Exact Low-rank Matrix Recovery via Nonconvex Mp-Minimization

Lingchen Kong(lchkong***at***bjtu.edu.cn)
Naihua Xiu(nhxiu***at***bjtu.edu.cn)

Abstract: The low-rank matrix recovery (LMR) arises in many fields such as signal and image processing, statistics, computer vision, system identification and control, and it is NP-hard. It is known that under some restricted isometry property (RIP) conditions we can obtain the exact low-rank matrix solution by solving its convex relaxation, the nuclear norm minimization. In this paper, we consider the nonconvex relaxations by introducing $M_p$-norm ($0

Keywords: low-rank matrix recovery, nonconvex Mp-minimization, restricted isometry constant,

Category 1: Nonlinear Optimization

Citation: Beijing Jiaotong University, April/2011

Download: [PDF]

Entry Submitted: 04/21/2011
Entry Accepted: 04/21/2011
Entry Last Modified: 04/21/2011

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