Stable sparse approximations via nonconvex optimization

TitleStable sparse approximations via nonconvex optimization
Publication TypeConference Paper
Year of Publication2008
AuthorsSaab, R., R. Chartrand, and O. Yilmaz
Conference NameAcoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Pagination3885 -3888
Date Publishedmar.
Keywordscompressible signals, lscrp minimization, minimisation, noise level, nonconvex optimization, numerical stability, restricted isometry constants, robustness, signal processing, stable sparse approximations

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 lt; 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 lt; 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.


a place of mind, The University of British Columbia

Electrical and Computer Engineering
2332 Main Mall
Vancouver, BC Canada V6T 1Z4
Tel +1.604.822.2872
Fax +1.604.822.5949

Emergency Procedures | Accessibility | Contact UBC | © Copyright 2020 The University of British Columbia