@article{mengucci_wisdom_2020,
	title = {{WISDoM}: a framework for the {Analysis} of {Wishart} distributed matrices},
	shorttitle = {{WISDoM}},
	url = {http://arxiv.org/abs/2001.10342},
	abstract = {WISDoM (Wishart Distributed Matrices) is a new framework for the characterization of symmetric positive-definite matrices associated to experimental samples, like covariance or correlation matrices, based on the Wishart distribution as a null model. WISDoM can be applied to tasks of supervised learning, like classification, even when such matrices are generated by data of different dimensionality (e.g. time series with same number of variables but different time sampling). In particular, we show the application of the method for 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.},
	urldate = {2020-02-02},
	journal = {arXiv:2001.10342 [physics, stat]},
	author = {Mengucci, Carlo and Remondini, Daniel and Giampieri, Enrico},
	month = jan,
	year = {2020},
	note = {arXiv: 2001.10342},
	keywords = {Physics - Data Analysis, Statistics and Probability, machine learning, mentions sympy},
}

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