Classification bayésienne non supervisée de données fonctionnelles
Bénédicte Fontez, Christophe Abraham, Damien Juery · HAL (Le Centre pour la Communication Scientifique Directe) · 2014
We are interested in unsupervised bayesian clustering for functional data. We generalize a data clusteringmodel based on the Dirichlet process, to functional data. Contrary to other papers making use of finite dimension, by decomposing curves into arbitrary basis functions, or by considering curves at their observation times, calculations are here realized onto complete curves in infinite dimension. The reproducing kernel Hilbert space theory permits us to derive densities of curves in respect to a gaussian measure. We thus propose a generalization to the algorithm Gibbs with Auxiliary Parameters, to the functional case. Performances are compared to those of an already existing method, and then discussed.