Fast aggregation of Student mixture models

Abstract : This paper deals with probabilistic models, that take the form of mixtures of Student distributions. Student distributions are known to be more statistically robust than Gaussian distributions, with regard to outliers (i.e. data that cannot be reasonnably explained by any component in the mixture and that do not justifiy an extra component. Our contribution is as follows : we show how several mixtures of Student distributions may be agregated into a single mixture, without resorting to sampling. The trick is that, as is well known, a Student distribution may be expressed as an infinite mixture of Gaussians, where the variances follow a Gamma distribution.
Type de document :
Communication dans un congrès
EURASIP. European Signal Processing Conference (Eusipco'2009), Aug 2009, Glasgow, United Kingdom. pp.312-216, 2009
Liste complète des métadonnées

Littérature citée [3 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00383948
Contributeur : Marc Gelgon <>
Soumis le : mercredi 29 octobre 2014 - 11:45:57
Dernière modification le : mercredi 11 avril 2018 - 02:00:55
Document(s) archivé(s) le : vendredi 30 janvier 2015 - 10:26:25

Fichier

ElAttard-eusipco2009.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

  • HAL Id : inria-00383948, version 1

Collections

Citation

Ali El Attar, Antoine Pigeau, Marc Gelgon. Fast aggregation of Student mixture models. EURASIP. European Signal Processing Conference (Eusipco'2009), Aug 2009, Glasgow, United Kingdom. pp.312-216, 2009. 〈inria-00383948〉

Partager

Métriques

Consultations de la notice

198

Téléchargements de fichiers

86