Clustering multivariate functional data in group-specific functional subspaces

Abstract : With the emergence of numerical sensors in many aspects of everyday life, there is an increasing need in analyzing multivariate functional data. This work fo-cuses on the clustering of those 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 functional 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-like algorithm is proposed for model inference and the choice of hyper-parameters is addressed through model selection. Numerical experiments on simulated datasets highlight the good performance of the proposed methodology compared to existing work. This algorithm is then applied for analyzing the pollution in U.S. cities for one year.
Type de document :
Pré-publication, Document de travail
2017
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Soumis le : jeudi 30 novembre 2017 - 12:30:51
Dernière modification le : jeudi 3 mai 2018 - 13:32:58

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  • HAL Id : hal-01652467, version 1

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Amandine Schmutz, Julien Jacques, Charles Bouveyron, Laurence Cheze, Pauline Martin. Clustering multivariate functional data in group-specific functional subspaces. 2017. 〈hal-01652467〉

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