Another interpretation of the EM algorithm for mixture distributions
AbstractThe EM algorithm for mixture problems can be interpreted as a method of coordinate descent on a particular objective function. This view of the iteration partially illuminates the relationship of EM to certain clustering techniques and explains global convergence properties of the algorithm without direct reference to an incomplete data framework.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 4 (1986)
Issue (Month): 2 (March)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Govaert, Gérard & Nadif, Mohamed, 2008. "Block clustering with Bernoulli mixture models: Comparison of different approaches," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3233-3245, February.
- Hu, Tianming & Sung, Sam Yuan, 2006. "A hybrid EM approach to spatial clustering," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1188-1205, March.
- Di Zio, Marco & Guarnera, Ugo & Rocci, Roberto, 2007. "A mixture of mixture models for a classification problem: The unity measure error," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2573-2585, February.
- Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer, vol. 13(2), pages 195-212, September.
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