Titre :

Classification of multivariate functional data on different domains with Partial Least Squares approaches

Auteur(s) :

Moindjié, Issam-Ali [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Dabo-Niang, Sophie [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]
Preda, Cristian [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]

Titre de la revue :

Statistics and Computing

Pagination :

5

Éditeur :

Springer Verlag (Germany)

Date de publication :

2024

ISSN :

0960-3174

Discipline(s) HAL :

Mathématiques [math]/Statistiques [math.ST]

Résumé en anglais : [en]

Classification of multivariate functional data is explored in this paper, particularly for functional data defined on different domains. Using the partial least squares (PLS) regression, we propose two classification ...
Lire la suite >Classification of multivariate functional data is explored in this paper, particularly for functional data defined on different domains. Using the partial least squares (PLS) regression, we propose two classification methods. The first one uses the equivalence between linear discriminant analysis and linear regression. The second is a decision tree based on the first technique. Moreover, we prove that multivariate PLS components can be estimated using univariate PLS components. This offers an alternative way to calculate PLS for multivariate functional data. Finite sample studies on simulated data and real data applications show that our algorithms are competitive with linear discriminant on principal components scores and black-boxes models.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL