• DocumentCode
    11186
  • Title

    Sparse Recovery by Means of Nonnegative Least Squares

  • Author

    Foucart, Simon ; Koslicki, David

  • Author_Institution
    Dept. of Math., Univ. of Georgia, Athens, GA, USA
  • Volume
    21
  • Issue
    4
  • fYear
    2014
  • fDate
    Apr-14
  • Firstpage
    498
  • Lastpage
    502
  • 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.
  • Keywords
    compressed sensing; least squares approximations; L1-minimization; compressive sensing problem; nonnegative least squares algorithm; orthogonal matching pursuit; sparse recovery; Compressed sensing; Least squares approximations; Linear matrix inequalities; MATLAB; Matching pursuit algorithms; Sparse matrices; Vectors; $k$-mer frequency matrices; ${ell _1}$-minimization; Adjacency matrices of bipartite graphs; Gaussian matrices; compressive sensing; nonnegative least squares; orthogonal matching pursuit; sparse recovery;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2014.2307064
  • Filename
    6750023
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=11186