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Compressed Sensing of Low-Rank Matrices: A Brief Survey on Efficient Algorithms  

Lee, Ki-Ryung (University of Illinois at Urbana- Champaign)
Ye, Jong-Chul (KAIST)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.46, no.5, 2009 , pp. 15-24 More about this Journal
Abstract
Compressed sensing addresses the recovery of a sparse vector from its few linear measurements. Recently, the success for the vector case has been extended to the matrix case. Compressed sensing of low-rank matrices solves the ill-posed inverse problem with fie low-rank prior. The problem can be formulated as either the rank minimization or the low-rank approximation. In this paper, we survey recently proposed efficient algorithms to solve these two formulations.
Keywords
Compressed sensing; singular value decomposition; restricted isometry property; low-rank approximation;
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