WISDoM: characterizing neurological timeseries with the Wishart distribution

no code implementations • 28 Jan 2020 • Carlo Mengucci, Daniel Remondini, Gastone Castellani, Enrico Giampieri

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).

EEG General Classification +2