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Convergence and Stability of a Class 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 a class of iteratively re-weighted least squares (IRLS) algorithms for sparse signal recovery in the presence of noise. We demonstrate a one-to-one correspondence between this class of algorithms and a class of Expectation-Maximization (EM) algor...

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Publicat a:IEEE Trans Signal Process
Autors principals: Babadi, Behtash, Ba, Demba, Purdon, Patrick L., Brown, Emery N.
Format: Artigo
Idioma:Inglês
Publicat: 2014
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Accés en línia:https://ncbi.nlm.nih.gov/pmc/articles/PMC4636042/
https://ncbi.nlm.nih.gov/pubmed/26549965
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TSP.2013.2287685
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