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17 juin 2019

Indéfini
Heure et lieu: 
11h, salle de réunion, bâtiment 210
Nom intervenant: 
Arnaud Gloaguen
Titre: 
Joint Matrix/Tensor Factorization with MGCCA
Résumé: 

Regularized Generalized Canonical Correlation Analysis (RGCCA) is a general multiblock data analysis framework that encompasses several important multivariate analysis methods such as principal component analysis, partial least squares regression and several versions of generalized canonical correlation analysis. In this paper, we extend RGCCA to the case where at least one block has a tensor structure. This method is called Multiway Generalized Canonical Correlation Analysis (MGCCA). Convergence properties of the MGCCA algorithm are studied and computation of higher-level components are discussed. The usefulness of MGCCA is shown on simulation and on the analysis of a cognitive study in human infants using high-density electro-encephalography (EEG).

Année: 
2019
Organisme intervenant: 
CentraleSupelec, L2S
Date du jour: 
Lundi, Juin 17, 2019


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