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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/11143
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dc.contributor.authorVlassis, N.en
dc.contributor.authorLikas, A.en
dc.date.accessioned2015-11-24T17:03:13Z-
dc.date.available2015-11-24T17:03:13Z-
dc.identifier.issn1083-4427-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11143-
dc.rightsDefault Licence-
dc.subjectexpectation-maximization (em) algorithmen
dc.subjectgaussian mixture modelingen
dc.subjectnumber of mixing kernelsen
dc.subjectprobability density function estimationen
dc.subjecttotal kurtosisen
dc.subjectweighted kurtosisen
dc.subjectprobabilistic neural networksen
dc.subjectmaximum-likelihooden
dc.subjectem algorithmen
dc.subjectcomponentsen
dc.subjectnumberen
dc.titleA kurtosis-based dynamic approach to Gaussian mixture modelingen
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.publicationDate1999-
heal.abstractWe address the problem of probability density function estimation using a Gaussian mixture model updated with the expectation-maximization (EM) algorithm. To deal with the case of an unknown number of mixing kernels, we define a new measure for Gaussian mixtures, called total kurtosis, which is based on the weighted sample kurtoses of the kernels. This measure provides an indication of how well the Gaussian mixture fits the data. Then we propose a new dynamic algorithm for Gaussian mixture density estimation which monitors the total kurtosis at each step of the Ehl algorithm in order to decide dynamically on the correct number of kernels and possibly escape from local maxima. We show the potential of our technique in approximating unknown densities through a series of examples with several density estimation problems.en
heal.journalNameIeee Transactions on Systems Man and Cybernetics Part a-Systems and Humansen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
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