Atomic Decomposition by Basis Pursuit. Chen, S. S., Donoho, D. L., & Saunders, M. A. SIAM Journal on Scientific Computing, 20(1):33–61, January, 1998. Publisher: Society for Industrial and Applied Mathematics![Atomic Decomposition by Basis Pursuit [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The time-frequency and time-scale communities have recently developed a large number of overcomplete waveform dictionaries—stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few. Decomposition into overcomplete systems is not unique, and several methods for decomposition have been proposed, including the method of frames (MOF), matching pursuit (MP), and, for special dictionaries, the best orthogonal basis (BOB).Basis pursuit (BP) is a principle for decomposing a signal into an "optimal"' superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions. We give examples exhibiting several advantages over MOF, MP, and BOB, including better sparsity and superresolution. BP has interesting relations to ideas in areas as diverse as ill-posed problems, abstract harmonic analysis, total variation denoising, and multiscale edge denoising.BP in highly overcomplete dictionaries leads to large-scale optimization problems. With signals of length 8192 and a wavelet packet dictionary, one gets an equivalent linear program of size 8192 by 212,992. Such problems can be attacked successfully only because of recent advances in linear and quadratic programming by interior-point methods. We obtain reasonable success with a primal-dual logarithmic barrier method and conjugate-gradient solver.
@article{chen_atomic_1998,
title = {Atomic {Decomposition} by {Basis} {Pursuit}},
volume = {20},
issn = {1064-8275},
url = {https://epubs.siam.org/doi/10.1137/S1064827596304010},
doi = {10.1137/S1064827596304010},
abstract = {The time-frequency and time-scale communities have recently developed a large number of overcomplete waveform dictionaries---stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few. Decomposition into overcomplete systems is not unique, and several methods for decomposition have been proposed, including the method of frames (MOF), matching pursuit (MP), and, for special dictionaries, the best orthogonal basis (BOB).Basis pursuit (BP) is a principle for decomposing a signal into an "optimal"' superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions. We give examples exhibiting several advantages over MOF, MP, and BOB, including better sparsity and superresolution. BP has interesting relations to ideas in areas as diverse as ill-posed problems, abstract harmonic analysis, total variation denoising, and multiscale edge denoising.BP in highly overcomplete dictionaries leads to large-scale optimization problems. With signals of length 8192 and a wavelet packet dictionary, one gets an equivalent linear program of size 8192 by 212,992. Such problems can be attacked successfully only because of recent advances in linear and quadratic programming by interior-point methods. We obtain reasonable success with a primal-dual logarithmic barrier method and conjugate-gradient solver.},
language = {en},
number = {1},
urldate = {2023-06-27},
journal = {SIAM Journal on Scientific Computing},
author = {Chen, Scott Shaobing and Donoho, David L. and Saunders, Michael A.},
month = jan,
year = {1998},
note = {Publisher: Society for Industrial and Applied Mathematics},
keywords = {\#Representation, /readed, Cosine packets, Denoising, Interior-point methods for linear programming, MATLAB code, Matching pursuit, Multiscale edges, Overcomplete signal representation, Time-frequency analysis, Time-scale analysis, Total variation denoising, Wavelet packets, Wavelets, ℓ1 norm optimization},
pages = {33--61},
}
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