  WISDoM (Wishart Distributed Matrices) is a new framework for the
quantification of deviation of symmetric positive-definite matrices associated
to experimental samples, like covariance or correlation matrices, from expected
ones governed by the Wishart distribution WISDoM can be applied to tasks of
supervised learning, like classification, in particular when such matrices are
generated by data of different dimensionality (e.g. time series with same
number of variables but different time sampling). We show the application of
the method in two different scenarios. The first is the ranking of features
associated to electro encephalogram (EEG) data with a time series design,
providing a theoretically sound approach for this type of studies. The second
is the classification of autistic subjects of the ABIDE study, using brain
connectivity measurements.
