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arXiv · 1506.02927

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

Juliette Spinnato·Marie-Christine Roubaud·Margaux Perrin·Emmanuel Maby·Jeremie Mattout·Boris Burle·Bruno Torrésani
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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.

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Juliette Spinnato, Marie-Christine Roubaud, Margaux Perrin, Emmanuel Maby, Jeremie Mattout, Boris Burle, Bruno Torrésani. 2015-06-09. Analyse discriminante matricielle descriptive. Application a l'étude de signaux EEG. https://arxiv.org/abs/1506.02927

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