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.

中文翻译：

---

WISDoM：用 Wishart 分布表征神经时间序列

WISDoM（Wishart 分布式矩阵）是一个框架，用于量化与实验样本相关的对称正定矩阵（例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 可应用于监督学习的任务，如分类，特别是当此类矩阵由不同维度的数据生成时（例如，具有相同变量数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场景中的应用。第一个是使用时间序列设计对与脑电图 (EEG) 数据相关的特征进行排序，为此类研究提供了理论上合理的方法。