Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/10897
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dc.contributor.authorConstantinopoulos, C.en
dc.contributor.authorTitsias, M. K.en
dc.contributor.authorLikas, A.en
dc.date.accessioned2015-11-24T17:01:15Z-
dc.date.available2015-11-24T17:01:15Z-
dc.identifier.issn0162-8828-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/10897-
dc.rightsDefault Licence-
dc.subjectmixture modelsen
dc.subjectfeature selectionen
dc.subjectmodel selectionen
dc.subjectbayesian approachen
dc.subjectvariational trainingen
dc.titleBayesian feature and model selection for Gaussian mixture modelsen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate2006-
heal.abstractWe 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.en
heal.journalNameIeee Transactions on Pattern Analysis and Machine Intelligenceen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)



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