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Row-Action Methods for Compressed Sensing

2006

Conference Paper

ei


Compressed Sensing uses a small number of random, linear measurements to acquire a sparse signal. Nonlinear algorithms, such as l1 minimization, are used to reconstruct the signal from the measured data. This paper proposes rowaction methods as a computational approach to solving the l1 optimization problem. This paper presents a specific rowaction method and provides extensive empirical evidence that it is an effective technique for signal reconstruction. This approach offers several advantages over interior-point methods, including minimal storage and computational requirements, scalability, and robustness.

Author(s): Sra, S. and Tropp, J.
Book Title: ICASSP 2006
Journal: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)
Pages: 868-871
Year: 2006
Month: May
Day: 0
Publisher: IEEE Operations Center

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ICASSP.2006.1660792
Event Name: IEEE International Conference on Acoustics, Speech and Signal Processing
Event Place: Toulouse, France

Address: Piscataway, NJ, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@inproceedings{5222,
  title = {Row-Action Methods for Compressed Sensing},
  author = {Sra, S. and Tropp, J.},
  journal = {Proceedings of the IEEE International Conference on  Acoustics, Speech and Signal Processing (ICASSP 2006)},
  booktitle = {ICASSP 2006},
  pages = {868-871},
  publisher = {IEEE Operations Center},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = may,
  year = {2006},
  month_numeric = {5}
}