DocumentCode :
3421423
Title :
Stable sparse approximations via nonconvex optimization
Author :
Saab, Rayan ; Chartrand, Rick ; Yilmaz, Özgür
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
3885
Lastpage :
3888
Abstract :
We present theoretical results pertaining to the ability of lscrp minimization to recover sparse and compressible signals from incomplete and noisy measurements. In particular, we extend the results of Candes, Romberg and Tao (2005) to the p < 1 case. Our results indicate that depending on the restricted isometry constants (see, e.g., Candes and Tao (2006; 2005)) and the noise level, lscrp minimization with certain values of p < 1 provides better theoretical guarantees in terms of stability and robustness than lscr1 minimization does. This is especially true when the restricted isometry constants are relatively large.
Keywords :
minimisation; numerical stability; signal processing; compressible signals; lscrp minimization; noise level; nonconvex optimization; restricted isometry constants; robustness; stable sparse approximations; Compressed sensing; Constraint optimization; Equations; Linear systems; Noise level; Noise robustness; Robust stability; Sampling methods; Sparse matrices; ℓp minimization; Compressed Sensing; Compressive Sampling; Sparse Recovery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4518502
Filename :
4518502
Link To Document :
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