Estimation of univariate Gaussian mixtures ...
Document type :
Communication dans un congrès avec actes
Title :
Estimation of univariate Gaussian mixtures for huge raw datasets by using binned datasets
Author(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]
Conference title :
JDS 2020 - 52ème Journées de Statistiques de la Société Française de Statistique
City :
Nice
Country :
France
Start date of the conference :
2020-05-25
English keyword(s) :
Big data
Binned data
Unsupervised learning
Green computing
Binned data
Unsupervised learning
Green computing
HAL domain(s) :
Statistiques [stat]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Nationale
Popular science :
Non
Comment :
Le congrès a été annulé mais les actes publiés
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