Mixture of Gaussians for Distance Estimation with Missing Data

Eirola, Emil; Lendasse, Amaury; Vandewalle, Vincent; Biernacki, Christophe

Type de document :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1016/j.neucom.2013.07.050

Titre :

Mixture of Gaussians for Distance Estimation with Missing Data

Auteur(s) :

Eirola, Emil [Auteur correspondant]
School of Electrical Engineering [Aalto Univ]
Lendasse, Amaury [Auteur]
Laboratory of Computer and Information Science [CIS]
Vandewalle, Vincent [Auteur]

MOdel for Data Analysis and Learning [MODAL]
Université de Lille, Droit et Santé
Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]

Titre de la revue :

Neurocomputing

Pagination :

32-42

Éditeur :

Elsevier

Date de publication :

2014-05-05

ISSN :

0925-2312

Mot(s)-clé(s) en anglais :

Missing data
distance estimation
mixture model

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. The experimental evaluation additionally shows that more accurately estimating distances leads to improved prediction performance for classification and regression tasks when used as inputs for a neural network.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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