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]

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]

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 >

Language :

Anglais

Popular science :

Non

Comment :

Special Issue: Herman Wold Medal Winners 2007-2009

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL