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
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;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2014.2307064
