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Soft Computing
Published in: Soft Computing 2/2011

01-02-2011 | Original Paper

Entropy-type classification maximum likelihood algorithms for mixture models

Authors: Chien-Yo Lai, Miin-Shen Yang

Published in: Soft Computing | Issue 2/2011

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Abstract

Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness index parameter. In this paper, we consider fuzzy CML with an entropy-regularization term to create an entropy-type CML algorithm. The proposed entropy-type CML is a parameter-free algorithm for mixture models. Some numerical and real-data comparisons show that the proposed method provides better results than some existing methods.
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Metadata
Title
Entropy-type classification maximum likelihood algorithms for mixture models
Authors
Chien-Yo Lai
Miin-Shen Yang
Publication date
01-02-2011
Publisher
Springer-Verlag
Published in
Soft Computing / Issue 2/2011
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-010-0560-8

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