Clustering multivariate functional data in group-specific functional subspaces

Amandine Schmutz (), Julien Jacques, Charles Bouveyron, Laurence Chèze and Pauline Martin
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Amandine Schmutz: Lim France
Julien Jacques: Université de Lyon, Lyon 2
Charles Bouveyron: Université Côte d’Azur
Laurence Chèze: Université de Lyon, Lyon 1
Pauline Martin: Lim France

Computational Statistics, 2020, vol. 35, issue 3, No 8, 1131 pages

Abstract: 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 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 functional latent mixture model which fits the data into 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 Expectation Maximization 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 works. This algorithm is then applied to the analysis of the pollution in French cities for 1 year.

Keywords: Multivariate functional curves; Multivariate functional principal component analysis; Model-based clustering; EM algorithm (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s00180-020-00958-4

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