On rank distribution classifiers for high-dimensional data

Spatial sign and rank-based methods have been studied in the recent literature, especially when the dimension is smaller than the sample size. In this paper, a classification method based on the distribution of rank functions for high-dimensional data is considered with extension to functional data. The method is fully nonparametric in nature. The performance of the classification method is illustrated in comparison with some other classifiers using simulated and real data sets. Supporting code in R are provided for computational implementation of the classification method that will be of use to others.

近年来的相关学术文献已对空间符号法（Spatial sign）与基于秩的方法开展了研究，尤其聚焦于维度小于样本量的场景。本文针对高维数据提出了一种基于秩函数分布的分类方法，并将其拓展至函数型数据领域。该方法本质上为完全非参数方法。本文通过模拟数据集与真实数据集，将所提分类方法与其他多款分类器进行对比实验，以展示其分类性能。此外，本文还提供了用于实现该分类方法的R语言配套代码，可供其他研究人员参考复用。