Clustering multivariate functional data in group-specific functional subspaces

Abstract : With the emergence of numerical sensors in many aspects of every- day life, there is an increasing need in analyzing multivariate functional data. This work focuses on the clustering of such functional data, in order to ease their modeling and understanding. To this end, a novel clustering technique for multivariate functional data is presented. This method is based on a func- tional latent mixture model which fits the data in group-specific functional subspaces through a multivariate functional principal component analysis. A family of parsimonious models is obtained by constraining model parameters within and between groups. An EM algorithm is proposed for model inference and the choice of hyper-parameters is addressed through model selection. Nu- merical experiments on simulated datasets highlight the good performance of the proposed methodology compared to existing works. This algorithm is then applied to the analysis of the pollution in French cities for one year.
Type de document :
Pré-publication, Document de travail
Liste complète des métadonnées

Littérature citée [29 références]  Voir  Masquer  Télécharger
Contributeur : Charles Bouveyron <>
Soumis le : mardi 17 juillet 2018 - 16:03:16
Dernière modification le : jeudi 17 janvier 2019 - 13:48:04


Fichiers produits par l'(les) auteur(s)


  • HAL Id : hal-01652467, version 2


Amandine Schmutz, Julien Jacques, Charles Bouveyron, Laurence Cheze, Pauline Martin. Clustering multivariate functional data in group-specific functional subspaces. 2018. 〈hal-01652467v2〉



Consultations de la notice


Téléchargements de fichiers