Mixture of Gaussians for Distance Estimation ...
Document type :
Article dans une revue scientifique
Permalink :
Title :
Mixture of Gaussians for Distance Estimation with Missing Data
Author(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)
Journal title :
Neurocomputing
Volume number :
131
Pages :
32-42
Publisher :
Elsevier
Publication date :
2014-05-05
ISSN :
0925-2312
HAL domain(s) :
Statistiques [stat]/Méthodologie [stat.ME]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CHU Lille
Université de Lille
Université de Lille
Submission date :
2020-06-08T14:11:28Z