Mixture of Gaussians for Distance Estimation ...
Type de document :
Article dans une revue scientifique
URL permanente :
Titre :
Mixture of Gaussians for Distance Estimation with Missing Data
Auteur(s) :
Eirola, E. [Auteur]
Lendasse, Amaury [Auteur]
Vandewalle, Vincent [Auteur]
Biernacki, Christophe [Auteur]
Lendasse, Amaury [Auteur]
Vandewalle, Vincent [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Biernacki, Christophe [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Titre de la revue :
Neurocomputing
Numéro :
131
Pagination :
32-42
Éditeur :
Elsevier
Date de publication :
2014-05-05
ISSN :
0925-2312
Discipline(s) HAL :
Statistiques [stat]/Méthodologie [stat.ME]
Résumé en anglais : [en]
The majority of all commonly used machine learning methods can not be applied directly to data sets with missing values. However, most such meth- ods only depend on the relative di erences between samples instead of their ...
Lire la suite >The majority of all commonly used machine learning methods can not be applied directly to data sets with missing values. However, most such meth- ods only depend on the relative di erences between samples instead of their particular values, and thus one useful approach is to directly estimate the pairwise distances between all samples in the data set. This is accomplished by tting a Gaussian mixture model to the data, and using it to derive estimates for the distances. Experimental simulations con rm that the pro- posed method provides accurate estimates compared to alternative methods for estimating distances.Lire moins >
Lire la suite >The majority of all commonly used machine learning methods can not be applied directly to data sets with missing values. However, most such meth- ods only depend on the relative di erences between samples instead of their particular values, and thus one useful approach is to directly estimate the pairwise distances between all samples in the data set. This is accomplished by tting a Gaussian mixture model to the data, and using it to derive estimates for the distances. Experimental simulations con rm that the pro- posed method provides accurate estimates compared to alternative methods for estimating distances.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CHU Lille
Université de Lille
Université de Lille
Date de dépôt :
2020-06-08T14:11:28Z
2020-06-09T09:19:16Z
2020-06-09T09:19:16Z
Fichiers
- documen
- Accès libre
- Accéder au document