Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery - implementation -

Justin sent me the followinga while ago and I am just getting back to civilization:

Hi Igor,



I thought that you and the readers of your blog may be interested in a new software implementation that we've recently put online (designed for sparse recovery in the presence of nonnegativity and simultaneous sparsity constraints).

The software can be downloaded from:

http://mr.usc.edu/download/nns-greedy/

The algorithms we've implemented are described in our recent paper:

D. Kim, J. P. Haldar.  Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse  Recovery. Signal Processing 2016. In Press.

http://dx.doi.org/10.1016/j.sigpro.2016.01.021

Thanks!

--

Justin Haldar
Assistant Professor
Signal and Image Processing Institute
Electrical Engineering and Biomedical Engineering 

University of Southern California http://mr.usc.edu/ 

Thanks  Justin ! Here is the paper:

 

Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery by Daeun Kim, Justin P. Haldar

This work proposes a family of greedy algorithms to jointly reconstruct a set of vectors that are (i) nonnegative and (ii) simultaneously sparse with a shared support set. The proposed algorithms generalize previous approaches that were designed to impose these constraints individually. Similar to previous greedy algorithms for sparse recovery, the proposed algorithms iteratively identify promising support indices. In contrast to previous approaches, the support index selection procedure has been adapted to prioritize indices that are consistent with both the nonnegativity and shared support constraints. Empirical results demonstrate for the first time that the combined use of simultaneous sparsity and nonnegativity constraints can substantially improve recovery performance relative to existing greedy algorithms that impose less signal structure.

 

 

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