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Recherche - Valorisation
Séminaire du CMLA : A coherent framework for learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data
le 18 avril 2019
12h-13h
Séminaire animé par Juliette Chevallier (CMAP-Polytechnique)
Juliette-Chevallier.jpg
Abstract: Longitudinal studies are powerful tools to achieve a better understanding of temporal progressions of biological or natural phenomenons. For instance, efforts in chemotherapy monitoring rely more and more on the understanding of the global disease progression and not only on punctual states of health. Mixed effects models have proved their efficiency in the study of longitudinal data sets, especially for medical purposes.
This talk presents a nonlinear mixed effects model which allows to estimate both a group-representative piecewise-geodesic trajectory and inter-individual variability. This model provides a generic and coherent framework for studying longitudinal manifold-valued data. Estimation is formulated as a well-defined and consistent Maximum A Posteriori (MAP). Numerically, due to the non-linearity of the proposed model, the MAP estimation of the parameters is performed through a stochastic version of the Expectation-Maximization algorithm.
I will present a new version of the Stochastic-Approximation EM (SAEM) algorithm which prevents convergence toward local minima.
- Type :
- Séminaires - conférences
- Lieu(x) :
- Campus de Cachan
Bâtiment Cournot, salle C103
Invited speaker
Centre de Mathématiques Appliquées, CMAP, École polytechnique.
Centre de Recherche des Cordeliers, CRC, Université Paris Descartes.
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