PLS Approach for clusterwise linear regression on functional data

Preda, Cristian; Saporta, Gilbert

Type de document :

Partie d'ouvrage

DOI :

10.1007/978-3-642-17103-1_17

Titre :

PLS Approach for clusterwise linear regression on functional data

Auteur(s) :

Preda, Cristian [Auteur]

MOdel for Data Analysis and Learning [MODAL]
Saporta, Gilbert [Auteur]
CEDRIC. Méthodes statistiques de data-mining et apprentissage [CEDRIC - MSDMA]

Éditeur(s) ou directeur(s) scientifique(s) :

D.Banks

Titre de l’ouvrage :

Classification, Clustering, and Data Mining Applications

Éditeur :

Springer
Springer Berlin Heidelberg

Lieu de publication :

Berlin, Heidelberg

Date de publication :

2004-01-01

Discipline(s) HAL :

Mathématiques [math]/Statistiques [math.ST]

Résumé en anglais : [en]

Partial Least Squares approach is used for the clusterwise linear regression algorithm when the set of predictor variables forms a L2 continuous stochastic process.The number of clusters is treated as unknown and the ...
Lire la suite >Partial Least Squares approach is used for the clusterwise linear regression algorithm when the set of predictor variables forms a L2 continuous stochastic process.The number of clusters is treated as unknown and the convergence of the clusterwise algorithm is discussed.The approach is compared with other methods via an application on stock-exchange data.Lire moins >

Langue :

Anglais

Audience :

Non spécifiée

Vulgarisation :

Non

Commentaire :

IXth Conference of the International Federation of Classification Societies

Collections :