  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.
