Sparse Regularization via Convex Analysis

Sparse approximate solutions to linear equations are classically obtained via L1 norm regularized least squares, but this method often underestimates the true solution. As an alternative to the L1 norm, this paper proposes a class of nonconvex penalty functions that maintain the convexity of the lea...

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Bibliographic Details

Published in:	IEEE transactions on signal processing Vol. 65; no. 17; pp. 4481 - 4494
Main Author:	Selesnick, Ivan
Format:	Journal Article
Language:	English
Published:	IEEE 01.09.2017
Subjects:	convex function Convex functions Convolution Cost function denoising Noise reduction optimization Signal processing algorithms sparse approximation Sparse regularization
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
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