The Influence of Genetic Initial Algorithm on the Highest Likelihood in Gaussian Mixture Model

Author

Wang, Jinjia ; Hong, Wenxue ; Li, Xin

Volume

1

fYear

0

fDate

0-0 0

Firstpage

3580

Lastpage

3583

Abstract

The EM algorithm is a familiar tool to get maximum likelihood parameter estimation in Gaussian mixture model. But the main drawback of EM is that its solution can highly depend on its initial values, and consequently produce sub-optical maximum likelihood estimates. Thus a genetic initialization algorithm (GIA) is proposed to overcome this limitation. K-mean, FCM, and GIA is compared based on several experiments on synthetic and real data sets. Analysis of the experimental results shows that the proposed GIA achieve the highest likelihood

Keywords

Gaussian processes; expectation-maximisation algorithm; genetic algorithms; parameter estimation; Gaussian mixture model; expectation-maximisation algorithm; genetic initial algorithm; genetic initialization algorithm; highest likelihood; maximum likelihood parameter estimation; Auditory system; Biomedical engineering; Biomedical signal processing; Electronic mail; Genetic algorithms; Intelligent control; Laboratories; Maximum likelihood estimation; Parameter estimation; Signal processing algorithms; EM algorithm; Gaussian mixture model; Genetic algorithm; Initialization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713036

Filename

1713036