Convergence and Stability of Iteratively Re-weighted Least Squares Algorithms

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
Published in:	IEEE transactions on signal processing Vol. 62; no. 1; pp. 183 - 195
Main Authors:	Ba, Demba, Babadi, Behtash, Purdon, Patrick L., Brown, Emery N.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.01.2014 Institute of Electrical and Electronics Engineers
Subjects:	Applied sciences Compressive sampling constrained maximum likelihood estimation Convergence Convex functions Detection, estimation, filtering, equalization, prediction Exact sciences and technology expectation-maximization algorithms Gaussian scale mixtures Information, signal and communications theory mathematical programming Noise Random variables Sampling, quantization Signal and communications theory Signal processing algorithms Signal, noise Stability analysis Telecommunications and information theory Mobile radiocommunication GSM system expectation-maximization algorithms Wireless telecommunication Iterative method Compressive sampling Weighting Gaussian process Signal restoration Gaussian scale mixtures Simulation Least squares method constrained maximum likelihood estimation Convergence rate Signal processing Signal reconstruction Maximum likelihood EM algorithm Compressed sensing Mathematical programming
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
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