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...

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Bibliographic Details
Main Authors: Babadi, Behtash (Contributor), Brown, Emery N. (Contributor), Ba, Demba E. (Contributor), Purdon, Patrick Lee (Contributor)
Other Authors: Harvard University- (Contributor), Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2014-05-01T16:00:50Z.
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