Enhanced low-rank matrix approximation

Ankit Parekh, Ivan W. Selesnick

Research output: Contribution to journalArticlepeer-review

79 Scopus citations

Abstract

This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with nonconvex regularization. We employ parameterized nonconvex penalty functions to estimate the nonzero singular values more accurately than the nuclear norm. A closed-form solution for the global optimum of the proposed objective function (sum of data fidelity and the nonconvex regularizer) is also derived. The solution reduces to singular value thresholding method as a special case. The proposed method is demonstrated for image denoising.

Original languageEnglish
Article number7420602
Pages (from-to)493-497
Number of pages5
JournalIEEE Signal Processing Letters
Volume23
Issue number4
DOIs
StatePublished - Apr 2016
Externally publishedYes

Keywords

  • Low-rank matrix
  • convex
  • image denoising
  • non-convex regularization
  • nuclear norm
  • optimization

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