  Nearest neighbor classifier is arguably the most simple and popular
nonparametric classifier available in the literature. However, due to the
concentration of pairwise distances and the violation of the neighborhood
structure, this classifier often suffers in high-dimension, low-sample size
(HDLSS) situations, especially when the scale difference between the competing
classes dominates their location difference. Several attempts have been made in
the literature to take care of this problem. In this article, we discuss some
of these existing methods and propose some new ones. We carry out some
theoretical investigations in this regard and analyze several simulated and
benchmark datasets to compare the empirical performances of proposed methods
with some of the existing ones.
