DocumentCode :
897085
Title :
Cyclic minimizers, majorization techniques, and the expectation-maximization algorithm: a refresher
Author :
Stoica, Petre ; Selén, Yngve
Author_Institution :
Dept. of Information Technol., Uppsala Univ., Sweden
Volume :
21
Issue :
1
fYear :
2004
fDate :
1/1/2004 12:00:00 AM
Firstpage :
112
Lastpage :
114
Abstract :
Many parameter estimation problems in signal processing can be reduced to the task of minimizing a function of the unknown parameters. This task is difficult owing to the existence of possibly local minima and the sharpness of the global minimum. In this article we review three approaches that can be used to minimize functions of the type encountered in parameter estimation problems. The first two approaches, the cyclic minimization and the majorization technique, are quite general, whereas the third one, the expectation-maximization (EM) algorithm, is tied to the use of the maximum likelihood (ML) method for parameter estimation. The article provides a quick refresher of the aforementioned approaches for a wide readership.
Keywords :
maximum likelihood estimation; parameter estimation; signal processing; EM algorithm; cyclic minimizers; expectation-maximization algorithm; majorization techniques; maximum likelihood method; parameter estimation; signal processing; Convergence; Expectation-maximization algorithms; Minimization methods; Partitioning algorithms; Signal processing algorithms;
fLanguage :
English
Journal_Title :
Signal Processing Magazine, IEEE
Publisher :
ieee
ISSN :
1053-5888
Type :
jour
DOI :
10.1109/MSP.2004.1267055
Filename :
1267055
Link To Document :
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