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Estimation of univariate Gaussian mixtures for huge raw datasets by using binned datasets

Filippo Antonazzo 1 Christophe Biernacki 1 Christine Keribin 2 
1 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
2 CELESTE - Statistique mathématique et apprentissage
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay
Abstract : 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.
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Submitted on : Wednesday, January 20, 2021 - 9:33:52 AM
Last modification on : Tuesday, April 19, 2022 - 3:23:53 PM


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  • HAL Id : hal-03082437, version 2


Filippo Antonazzo, Christophe Biernacki, Christine Keribin. Estimation of univariate Gaussian mixtures for huge raw datasets by using binned datasets. JDS 2020 - 52ème Journées de Statistiques de la Société Française de Statistique, May 2020, Nice, France. ⟨hal-03082437v2⟩



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