Estimation of univariate Gaussian mixtures ...
Type de document :
Communication dans un congrès avec actes
Titre :
Estimation of univariate Gaussian mixtures for huge raw datasets by using binned datasets
Auteur(s) :
Antonazzo, Filippo [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Keribin, Christine [Auteur]
Statistique mathématique et apprentissage [CELESTE]
MOdel for Data Analysis and Learning [MODAL]
Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Keribin, Christine [Auteur]
Statistique mathématique et apprentissage [CELESTE]
Titre de la manifestation scientifique :
JDS 2020 - 52ème Journées de Statistiques de la Société Française de Statistique
Ville :
Nice
Pays :
France
Date de début de la manifestation scientifique :
2020-05-25
Mot(s)-clé(s) en anglais :
Big data
Binned data
Unsupervised learning
Green computing
Binned data
Unsupervised learning
Green computing
Discipline(s) HAL :
Statistiques [stat]
Résumé en anglais : [en]
Popularity of unsupervised learning is magnified by the regular increase of sample sizes. Indeed, it provides opportunity to reveal information previously out of scope. However, the volume of data leads to some issues ...
Lire la suite >Popularity of unsupervised learning is magnified by the regular increase of sample sizes. Indeed, it provides opportunity to reveal information previously out of scope. However, the volume of data leads to some issues related to prohibitive calculation times and also to high energy consumption and the need of high computational ressources. Resorting to binned data depending on an adaptive grid is expected to give proper answer to such green computing issues while not harming the related estimation issues. A first attempt is conducted in the context of univariate Gaussian mixtures, included a numerical illustration and some theoretical advances.Lire moins >
Lire la suite >Popularity of unsupervised learning is magnified by the regular increase of sample sizes. Indeed, it provides opportunity to reveal information previously out of scope. However, the volume of data leads to some issues related to prohibitive calculation times and also to high energy consumption and the need of high computational ressources. Resorting to binned data depending on an adaptive grid is expected to give proper answer to such green computing issues while not harming the related estimation issues. A first attempt is conducted in the context of univariate Gaussian mixtures, included a numerical illustration and some theoretical advances.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Nationale
Vulgarisation :
Non
Commentaire :
Le congrès a été annulé mais les actes publiés
Collections :
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