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Title: SPARSE REPRESENTATIONS WITH DATA FIDELITY TERM VIA AN ITERATIVELY REWEIGHTED LEAST SQUARES ALGORITHM

Abstract

Basis Pursuit and Basis Pursuit Denoising, well established techniques for computing sparse representations, minimize an {ell}{sup 2} data fidelity term subject to an {ell}{sup 1} sparsity constraint or regularization term on the solution by mapping the problem to a linear or quadratic program. Basis Pursuit Denoising with an {ell}{sup 1} data fidelity term has recently been proposed, also implemented via a mapping to a linear program. They introduce an alternative approach via an iteratively Reweighted Least Squares algorithm, providing greater flexibility in the choice of data fidelity term norm, and computational advantages in certain circumstances.

Authors:
 [1];  [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1000493
Report Number(s):
LA-UR-07-0078
TRN: US201101%%596
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS; ALGORITHMS; LEAST SQUARE FIT; ITERATIVE METHODS

Citation Formats

WOHLBERG, BRENDT, and RODRIGUEZ, PAUL. SPARSE REPRESENTATIONS WITH DATA FIDELITY TERM VIA AN ITERATIVELY REWEIGHTED LEAST SQUARES ALGORITHM. United States: N. p., 2007. Web. doi:10.2172/1000493.
WOHLBERG, BRENDT, & RODRIGUEZ, PAUL. SPARSE REPRESENTATIONS WITH DATA FIDELITY TERM VIA AN ITERATIVELY REWEIGHTED LEAST SQUARES ALGORITHM. United States. https://doi.org/10.2172/1000493
WOHLBERG, BRENDT, and RODRIGUEZ, PAUL. 2007. "SPARSE REPRESENTATIONS WITH DATA FIDELITY TERM VIA AN ITERATIVELY REWEIGHTED LEAST SQUARES ALGORITHM". United States. https://doi.org/10.2172/1000493. https://www.osti.gov/servlets/purl/1000493.
@article{osti_1000493,
title = {SPARSE REPRESENTATIONS WITH DATA FIDELITY TERM VIA AN ITERATIVELY REWEIGHTED LEAST SQUARES ALGORITHM},
author = {WOHLBERG, BRENDT and RODRIGUEZ, PAUL},
abstractNote = {Basis Pursuit and Basis Pursuit Denoising, well established techniques for computing sparse representations, minimize an {ell}{sup 2} data fidelity term subject to an {ell}{sup 1} sparsity constraint or regularization term on the solution by mapping the problem to a linear or quadratic program. Basis Pursuit Denoising with an {ell}{sup 1} data fidelity term has recently been proposed, also implemented via a mapping to a linear program. They introduce an alternative approach via an iteratively Reweighted Least Squares algorithm, providing greater flexibility in the choice of data fidelity term norm, and computational advantages in certain circumstances.},
doi = {10.2172/1000493},
url = {https://www.osti.gov/biblio/1000493}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 08 00:00:00 EST 2007},
month = {Mon Jan 08 00:00:00 EST 2007}
}