Classification of high-dimensional low sample size (HDLSS) data poses a challenge in a variety of real-world situations, such as gene expression studies, cancer research, and medical imaging. This article presents the development and analysis of some classifiers that are specifically designed for HDLSS data. These classifiers are free of tuning parameters and are robust, in the sense that they are devoid of any moment conditions of the underlying data distributions. It is shown that they yield perfect classification in the HDLSS asymptotic regime, under some fairly general conditions. The comparative performance of the proposed classifiers is also investigated. Our theoretical results are supported by extensive simulation studies and real data analysis, which demonstrate promising advantages of the proposed classification techniques over several widely recognized methods.

翻译：高维低样本量数据分类在基因表达研究、癌症研究和医学影像等多种实际场景中构成挑战。本文介绍了一些专门针对高维低样本量数据设计的分类器的开发与分析。这些分类器无需调整参数，且具有鲁棒性，即不依赖于底层数据分布的任何矩条件。研究表明，在相当一般的条件下，这些分类器在高维低样本量渐近框架下能实现完美分类。我们还对所提出分类器的比较性能进行了研究。广泛模拟研究和真实数据分析支持了我们的理论结果，证明了所提分类技术相较于多种广泛认可方法的显著优势。