Add to ORKGInternational audienceModel-based clustering is considered for Gaussian multivariate functional data as an extension of the univariate functional setting. Principal components analysis is introduced and used to define an approximation of the notion of density for multivariate functional data. An EM like algorithm is proposed to estimate the parameters of the reduced model. Application on climatology data illustrates the method
In this article we propose a penalized clustering method for large-scale data with multiple covariat...
International audienceHigh dimensional data clustering is an increasingly interesting topic in the s...
This thesis provides novel methodologies for functional Principal Component Analysis of dependent t...
International audienceThis paper proposes the first model-based clustering algorithm for multivariat...
International audienceWith the emergence of numerical sensors in many aspects of every- day life, th...
[[abstract]]A novel multivariate k-centers functional clustering algorithm for the multivariate func...
[[abstract]]We propose a multivariate k-centers functional clustering algorithm for the multivariate...
International audienceThis work develops a general procedure for clustering functional data which ad...
In this paper we propose a novel clustering method for functional data based on the principal curve ...
This paper presents a new model-based generalized functional clustering method for discrete longitud...
This work develops a general procedure for clustering functional data which adapts the efficient clu...
International audienceComplex data analysis is a central topic of modern statistics and learning sys...
National audienceThe emergence of numerical sensors in many aspects of everyday life leadsto an incr...
The problem of clustering functional data is addressed. Results on principal points (cluster means f...
In this article we propose a penalized clustering method for large-scale data with multiple covariat...
International audienceHigh dimensional data clustering is an increasingly interesting topic in the s...
This thesis provides novel methodologies for functional Principal Component Analysis of dependent t...
International audienceThis paper proposes the first model-based clustering algorithm for multivariat...
International audienceWith the emergence of numerical sensors in many aspects of every- day life, th...
[[abstract]]A novel multivariate k-centers functional clustering algorithm for the multivariate func...
[[abstract]]We propose a multivariate k-centers functional clustering algorithm for the multivariate...
International audienceThis work develops a general procedure for clustering functional data which ad...
In this paper we propose a novel clustering method for functional data based on the principal curve ...
This paper presents a new model-based generalized functional clustering method for discrete longitud...
This work develops a general procedure for clustering functional data which adapts the efficient clu...
International audienceComplex data analysis is a central topic of modern statistics and learning sys...
National audienceThe emergence of numerical sensors in many aspects of everyday life leadsto an incr...
The problem of clustering functional data is addressed. Results on principal points (cluster means f...
In this article we propose a penalized clustering method for large-scale data with multiple covariat...
International audienceHigh dimensional data clustering is an increasingly interesting topic in the s...
This thesis provides novel methodologies for functional Principal Component Analysis of dependent t...