Sparse Recovery by Means of Nonnegative Least Squares Academic Article

abstract

• This letter demonstrates that sparse recovery can be achieved by an 1-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. 2013 IEEE.

authors

• Foucart, Simon

published proceedings

• IEEE SIGNAL PROCESSING LETTERS

author list (cited authors)

• Foucart, S., & Koslicki, D.

citation count

• 48

complete list of authors

• Foucart, Simon||Koslicki, David

publication date

• April 2014

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Signal Processing Letters  Journal

keywords

• Adjacency Matrices Of Bipartite Graphs
• Compressive Sensing
• Gaussian Matrices
• K-mer Frequency Matrices
• L(1)-minimization
• Nonnegative Least Squares
• Orthogonal Matching Pursuit
• Sparse Recovery

Digital Object Identifier (DOI)

• 10.1109/LSP.2014.2307064

start page

• 498

end page

• 502

volume

• 21

issue

• 4

URL

• http://dx.doi.org/10.1109/lsp.2014.2307064