Document Detail

Bayesian feature and model selection for Gaussian mixture models.
MedLine Citation:
PMID:  16724595     Owner:  NLM     Status:  MEDLINE    
We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of a mixture model formulation that takes into account the saliency of the features and a Bayesian approach to mixture learning that can be used to estimate the number of mixture components. The proposed learning algorithm follows the variational framework and can simultaneously optimize over the number of components, the saliency of the features, and the parameters of the mixture model. Experimental results using high-dimensional artificial and real data illustrate the effectiveness of the method.
Constantinos Constantinopoulos; Michalis K Titsias; Aristidis Likas
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on pattern analysis and machine intelligence     Volume:  28     ISSN:  0162-8828     ISO Abbreviation:  IEEE Trans Pattern Anal Mach Intell     Publication Date:  2006 Jun 
Date Detail:
Created Date:  2006-05-26     Completed Date:  2006-06-20     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9885960     Medline TA:  IEEE Trans Pattern Anal Mach Intell     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1013-8     Citation Subset:  IM    
Department of Computer Science, University of Ioannina, Ioannina GR 45110, Greece.
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MeSH Terms
Artificial Intelligence*
Computer Simulation
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Information Storage and Retrieval / methods*
Models, Statistical
Normal Distribution
Pattern Recognition, Automated / methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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