%0 Journal Article
%T Clustering multivariate functional data in group-specific functional subspaces
%+ Lim France
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%+ Laboratoire Jean Alexandre Dieudonné (LJAD)
%+ Modèles et algorithmes pour l’intelligence artificielle (MAASAI)
%+ COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)
%+ Inria Sophia Antipolis - Méditerranée (CRISAM)
%+ Laboratoire de Biomécanique et Mécanique des Chocs (LBMC UMR T9406 )
%A Schmutz, Amandine
%A Jacques, Julien
%A Bouveyron, Charles
%A Chèze, Laurence
%A Martin, Pauline
%< avec comité de lecture
%@ 0943-4062
%J Computational Statistics
%I Springer Verlag
%V 35
%N 3
%P 1101-1131
%8 2020-02-12
%D 2020
%R 10.1007/s00180-020-00958-4
%K Multivariate functional data
%K model-based clustering
%K EM-algorithm
%K multivariate functional principal component analysis
%Z Mathematics [math]/Statistics [math.ST]Journal articles
%X 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.
%G English
%2 https://inria.hal.science/hal-01652467v3/document
%2 https://inria.hal.science/hal-01652467v3/file/Multivariate-funHDDC.pdf
%L hal-01652467
%U https://inria.hal.science/hal-01652467
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