| Bayesian feature and model selection for Gaussian mixture models. | |
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MedLine Citation:
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PMID: 16724595 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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Constantinos Constantinopoulos; Michalis K Titsias; Aristidis Likas |
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Publication Detail:
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Type: Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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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:
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Created Date: 2006-05-26 Completed Date: 2006-06-20 Revised Date: 2006-11-15 |
Medline Journal Info:
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Nlm Unique ID: 9885960 Medline TA: IEEE Trans Pattern Anal Mach Intell Country: United States |
Other Details:
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Languages: eng Pagination: 1013-8 Citation Subset: IM |
Affiliation:
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Department of Computer Science, University of Ioannina, Ioannina GR 45110, Greece. ccostas@cs.uoi.gr |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* 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* |
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