WISDoM: Characterizing Neurological Time Series With the Wishart Distribution

Frontiers in Neuroinformatics

Carlo Mengucci¹  Gastone Castellani²  Enrico Giampieri²  Daniel Remondini³ 

[1] Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy;Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy;Department of Physics and Astronomy, University of Bologna, Bologna, Italy;

关键词: supervised learning;    time series;    null model;    feature selection;    classification;    Wishart distribution;   

DOI  :  10.3389/fninf.2020.611762

来源: DOAJ

【 摘 要 】

WISDoM (Wishart Distributed Matrices) is a framework for the quantification of deviation of symmetric positive-definite matrices associated with experimental samples, such as 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 with 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 Autism Brain Imaging Data Exchange study using brain connectivity measurements.

【 授权许可】

Unknown