title: An extension of the Canonical Correlation Analysis to the case of multiple observations of two groups of variables.
abstract: In this contribution we present a method that extends the Canonical Correlation Analysis for two groups of variables to the case of multiple conditions. Contrary to the extensions in literature based on augmenting the number of variable groups, the addition of conditions allows for a more robust estimate of the canonical correlation structure inherently present in the data. Algorithms to solve the estimation problem are based on joint approximate diagonalization algorithms for matrix sets. Simulations show the performance of the proposed method under two different scenarios: the calculation of a latent canonical structure and the estimation of a bilinear mixture model.
authors.affiliation: Vision and Brain Signal processing (ViBs) research group, GIPSA Lab, INPG/UMR 5216 CNRS, BP 46, 961, Rue de la Houille Blanche, 38402 Saint Martin d'Hères, France. roland.phlypo@gipsa-lab.grenoble-inp.fr
authors.forename: Ronald
authors.initials: R
authors.lastname: Phlypo
authors.forename: Marco
authors.initials: M
authors.lastname: Congedo
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000379:methods; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D012815:Signal Processing, Computer-Assisted