DocumentCode
1446469
Title
Bayesian approaches to Gaussian mixture modeling
Author
Roberts, Stephen J. ; Husmeier, Dirk ; Rezek, Iead ; Penny, William
Author_Institution
Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK
Volume
20
Issue
11
fYear
1998
fDate
11/1/1998 12:00:00 AM
Firstpage
1133
Lastpage
1142
Abstract
A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an “optimal” number of components in the model and so partition data sets. The performance of the Bayesian method is compared to other methods of optimal model selection and found to give good results. The methods are tested on synthetic and real data sets
Keywords
Bayes methods; Hessian matrices; covariance matrices; parameter estimation; pattern clustering; unsupervised learning; Bayesian approaches; Gaussian mixture modeling; optimal model selection; Bayesian methods; Distributed computing; Equations; Parameter estimation; Probability density function; Roentgenium; Taylor series; Testing; Unsupervised learning;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.730550
Filename
730550
Link To Document