论文标题
智慧：用Wishart分布来表征神经学的时间
WISDoM: characterizing neurological timeseries with the Wishart distribution
论文作者
论文摘要
智慧（WishArt分布式矩阵）是量化与实验样本相关的对称积极准矩阵偏离偏差的新框架，这些矩阵（例如协方差矩阵或相关矩阵）受期望分布智慧控制的矩阵（例如，可以应用于各种矩阵的时间范围，尤其是在不同的时间范围内，都可以应用于监督学习的任务（尤其是在不同的时间范围内）。采样）。我们在两种不同的情况下显示了该方法的应用。首先是与时间序列设计相关的与电脑电图（EEG）数据相关的特征的排名，为此类研究提供了理论上合理的方法。第二个是使用大脑连通性测量值的自闭症研究对象的分类。
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.
