Convergence and Stability of Iteratively Re-weighted Least Squares Algorithms for Sparse Signal Recovery in the Presence of Noise

Convergence and Stability of Iteratively Re-weighted Least Squares Algorithms for Sparse Signal Recovery in the Presence of Noise

In this paper, we study the theoretical properties of iteratively re-weighted least squares (IRLS) algorithms and their utility in sparse signal recovery in the presence of noise. We demonstrate a one-to-one correspondence between the IRLS algorithms and a class of Expectation-Maximization (EM) algo...

Full description

Bibliographic Details
Main Authors: Babadi, Behtash, Brown, Emery N., Ba, Demba E., Purdon, Patrick Lee
Other Authors: Harvard University--MIT Division of Health Sciences and Technology
Format: Article
Language:en_US
Published: Institute of Electrical and Electronics Engineers (IEEE) 2014
Online Access:http://hdl.handle.net/1721.1/86328
https://orcid.org/0000-0001-5651-5060
https://orcid.org/0000-0003-2668-7819

Internet

http://hdl.handle.net/1721.1/86328
https://orcid.org/0000-0001-5651-5060
https://orcid.org/0000-0003-2668-7819

Similar Items