  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.
