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Clustering multivariate functional data in group-specific functional subspaces
Clustering multivariate functional data in group-specific functional subspaces
- Source :
- Computational Statistics, Computational Statistics, Springer Verlag, 2020, ⟨10.1007/s00180-020-00958-4⟩, Computational Statistics, 2020, ⟨10.1007/s00180-020-00958-4⟩
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- International audience; 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.
- Subjects :
- Statistics and Probability
Multivariate statistics
Computer science
EM-algorithm
computer.software_genre
01 natural sciences
010104 statistics & probability
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
0502 economics and business
Expectation–maximization algorithm
0101 mathematics
Cluster analysis
050205 econometrics
Functional principal component analysis
Group (mathematics)
Model selection
05 social sciences
model-based clustering
Mixture model
Linear subspace
multivariate functional principal component analysis
Computational Mathematics
Multivariate functional data
Data mining
Statistics, Probability and Uncertainty
computer
Subjects
Details
- Language :
- English
- ISSN :
- 09434062 and 16139658
- Database :
- OpenAIRE
- Journal :
- Computational Statistics, Computational Statistics, Springer Verlag, 2020, ⟨10.1007/s00180-020-00958-4⟩, Computational Statistics, 2020, ⟨10.1007/s00180-020-00958-4⟩
- Accession number :
- edsair.doi.dedup.....ad01e761a3aac8f16f3b6f3c53e38389
- Full Text :
- https://doi.org/10.1007/s00180-020-00958-4⟩