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:
-
- 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}
}
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