Probabilistic model-based discriminant analysis and clustering methods in chemometrics

Abstract : In chemometrics, the supervised and unsupervised classification of high-dimensional data has become a recurrent problem. Model-based techniques for discriminant analysis and clustering are popular tools, which are renowned for their probabilistic foundations and their flexibility. However, classical model-based techniques show a disappoint- ing behaviour in high-dimensional spaces, which up to now have been limited in their use within chemometrics. The recent developments in model-based classification overcame these drawbacks and enabled the efficient classifica- tion of high-dimensional data, even in the 'small n / large p' condition. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modelling, subspace classifica- tion methods and classification methods based on variable selection. The use of these model-based methods is also illustrated on real-world classification problems in chemometrics using R packages.
Type de document :
Article dans une revue
Journal of Chemometrics, Wiley, 2013, in press. 〈10.1002/cem.2560〉
Liste complète des métadonnées

Littérature citée [75 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-00875883
Contributeur : Charles Bouveyron <>
Soumis le : mercredi 23 octobre 2013 - 09:15:55
Dernière modification le : mardi 10 octobre 2017 - 11:22:04
Document(s) archivé(s) le : vendredi 7 avril 2017 - 17:21:39

Fichier

final_JoChemo.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Charles Bouveyron. Probabilistic model-based discriminant analysis and clustering methods in chemometrics. Journal of Chemometrics, Wiley, 2013, in press. 〈10.1002/cem.2560〉. 〈hal-00875883〉

Partager

Métriques

Consultations de
la notice

218

Téléchargements du document

300