WISDoM：用Wishart分布表征神经时间序列
WISDoM: characterizing neurological timeseries with the Wishart distribution
摘要：
智慧（Wishart Distributed Matrices）是一个新的框架，用于量化与实验样本相关的对称正定矩阵（如协方差或相关矩阵）与由Wishart分布控制的预期矩阵的偏差WISDoM可以应用于监督学习的任务，如分类，特别是当这种矩阵由不同维度的数据生成时（例如，具有相同数量的变量但不同时间采样的时间序列）。我们展示了该方法在两种不同情况下的应用。首先是与脑电图（EEG）数据的时间序列设计相关的功能的排名，提供了一个理论上健全的方法，这类研究。第二个是使用大脑连接测量对ABIDE研究中的自闭症受试者进行分类。
英文摘要：
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.
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