Sparse recovery by means of nonnegative least squares

Simon Foucart; David Koslicki

doi:10.1109/LSP.2014.2307064

Sparse recovery by means of nonnegative least squares

Simon Foucart
, David Koslicki

Research output: Contribution to journal › Article › peer-review

59 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 language	English (US)
Article number	6750023
Pages (from-to)	498-502
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	21
Issue number	4
DOIs	https://doi.org/10.1109/LSP.2014.2307064
State	Published - Apr 2014

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/LSP.2014.2307064

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Sparse recovery by means of nonnegative least squares

Abstract

All Science Journal Classification (ASJC) codes

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