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Classification of high-dimensional low sample size (HDLSS) data poses achallenge in a variety of real-world situations, such as gene expressionstudies, cancer research, and medical imaging. This article presents thedevelopment and analysis of some classifiers that are specifically designed forHDLSS data. These classifiers are free of tuning parameters and are robust, inthe sense that they are devoid of any moment conditions of the underlying datadistributions. It is shown that they yield perfect classification in the HDLSSasymptotic regime, under some fairly general conditions. The comparativeperformance of the proposed classifiers is also investigated. Our theoreticalresults are supported by extensive simulation studies and real data analysis,which demonstrate promising advantages of the proposed classificationtechniques over several widely recognized methods.

Robust Classification of High-Dimensional Data using Data-Adaptive Energy Distance