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Paul Rodríguez and Brendt Wohlberg, "An Efficient Algorithm for Sparse Representations with lpData Fidelity Term", in Proceedings of 4th IEEE Andean Technical Conference (ANDESCON), (Cusco, Perú), Oct 2008

Abstract

Basis Pursuit (BP) and Basis Pursuit Denoising (BPDN), well established techniques for computing sparse representations, minimize an l2 data fidelity term, subject to an l1 sparsity constraint or regularization term, by mapping the problem to a linear or quadratic program. BPDN with an l1 data fidelity term has recently been proposed, also implemented via a mapping to a linear program. We introduce an alternative approach via an Iteratively Reweighted Least Squares algorithm, providing computational advantages and greater flexibility in the choice of data fidelity term norm.

BibTeX Entry

@inproceedings{rodriguez-2008-sparse,
author = {Paul Rodr\'{i}guez and Brendt Wohlberg},
title = {An Efficient Algorithm for Sparse Representations with $l^{p}$ Data Fidelity Term},
year = {2008},
month = Oct,
urlpdf = {http://brendt.wohlberg.net/publications/pdf/rodriguez-2008-sparse.pdf},
booktitle = {Proceedings of 4th IEEE Andean Technical Conference (ANDESCON)},
address = {Cusco, Per\'{u}}
}