论文标题
简约的特征提取方法：用广义偏斜-T扩展鲁棒的概率投影
Parsimonious Feature Extraction Methods: Extending Robust Probabilistic Projections with Generalized Skew-t
论文作者
论文摘要
我们提出了对Student-T概率主要成分方法的新概括，该方法：（1）解释了观察数据的不对称分布； （2）是用于分组和广义多重自由结构的框架，它为观察数据中的边缘尾巴依赖性组建模提供了更灵活的方法； （3）分开误差术语和因素的尾巴效应。新功能提取方法是在不完整的数据设置中得出的，以有效处理观察矢量中缺少值的存在。我们讨论了算法的各种特殊情况，这是对生成数据的过程的简化假设的结果。新框架的适用性在一个数据集中说明，该数据集由市值最高的加密货币组成。
We propose a novel generalisation to the Student-t Probabilistic Principal Component methodology which: (1) accounts for an asymmetric distribution of the observation data; (2) is a framework for grouped and generalised multiple-degree-of-freedom structures, which provides a more flexible approach to modelling groups of marginal tail dependence in the observation data; and (3) separates the tail effect of the error terms and factors. The new feature extraction methods are derived in an incomplete data setting to efficiently handle the presence of missing values in the observation vector. We discuss various special cases of the algorithm being a result of simplified assumptions on the process generating the data. The applicability of the new framework is illustrated on a data set that consists of crypto currencies with the highest market capitalisation.
