Analyse discriminante matricielle descriptive. Application a l'\'etude de signaux EEG

Abstract

We focus on the descriptive approach to linear discriminant analysis for matrix-variate data in the binary case. Under a separability assumption on row and column variability, the most discriminant linear combinations of rows and columns are determined by the singular value decomposition of the difference of the class-averages with the Mahalanobis metric in the row and column spaces. This approach provides data representations of data in two-dimensional or three-dimensional plots and singles out discriminant components. An application to electroencephalographic multi-sensor signals illustrates the relevance of the method.