Gaussian mixture models for the classification ...
Document type :
Compte-rendu et recension critique d'ouvrage
DOI :
Title :
Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data
Author(s) :
Jacques, Julien [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Bouveyron, Charles [Auteur]
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
Girard, Stéphane [Auteur]
Modelling and Inference of Complex and Structured Stochastic Systems [MISTIS]
Devos, Olivier [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Duponchel, Ludovic [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Bouveyron, Charles [Auteur]
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
Girard, Stéphane [Auteur]
Modelling and Inference of Complex and Structured Stochastic Systems [MISTIS]
Devos, Olivier [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Duponchel, Ludovic [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Journal title :
Journal of Chemometrics
Pages :
719-727
Publisher :
Wiley
Publication date :
2010-11
ISSN :
0886-9383
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]
Statistiques [stat]/Théorie [stat.TH]
English abstract : [en]
In this work, a family of generative Gaussian models designed for the supervised classification of high-dimensional data is presented as well as the associated classification method called High Dimensional Discriminant ...
Show more >In this work, a family of generative Gaussian models designed for the supervised classification of high-dimensional data is presented as well as the associated classification method called High Dimensional Discriminant Analysis (HDDA). The advantages of these Gaussian models are: i) the representation of the input density model is smooth; ii) the data of each class are modeled in a specific subspace of low dimensionality; iii) each class may have its own covariance structure; iv) regularization is coupled to the classification criterion to avoid data over-fitting. To illustrate the abilities of the method, HDDA is applied on complex high-dimensional multi-class classification problems in mid-infrared and near infrared spectroscopy and compared to state-of-the-art methods.Show less >
Show more >In this work, a family of generative Gaussian models designed for the supervised classification of high-dimensional data is presented as well as the associated classification method called High Dimensional Discriminant Analysis (HDDA). The advantages of these Gaussian models are: i) the representation of the input density model is smooth; ii) the data of each class are modeled in a specific subspace of low dimensionality; iii) each class may have its own covariance structure; iv) regularization is coupled to the classification criterion to avoid data over-fitting. To illustrate the abilities of the method, HDDA is applied on complex high-dimensional multi-class classification problems in mid-infrared and near infrared spectroscopy and compared to state-of-the-art methods.Show less >
Language :
Anglais
Popular science :
Non
Comment :
Special Issue: Herman Wold Medal Winners 2007-2009
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