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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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| Publié dans: | IEEE Trans Signal Process |
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| Auteurs principaux: | , , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
2014
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| Sujets: | |
| Accès en ligne: | 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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