Sparse recovery by means of nonnegative least squares

Simon Foucart, David Koslicki

Research output: Contribution to journalArticlepeer-review

57 Scopus citations

Abstract

This letter demonstrates that sparse recovery can be achieved by an ℓ1-minimization ersatz easily implemented using a conventional nonnegative least squares algorithm. A connection with orthogonal matching pursuit is also highlighted. The preliminary results call for more investigations on the potential of the method and on its relations to classical sparse recovery algorithms.

Original languageEnglish (US)
Article number6750023
Pages (from-to)498-502
Number of pages5
JournalIEEE Signal Processing Letters
Volume21
Issue number4
DOIs
StatePublished - Apr 2014

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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