  Classification is a classic problem but encounters lots of challenges when
dealing with a large number of features, which is common in many modern
applications, such as identifying tumor sub-types from genomic data or
categorizing customer attitudes based on on-line reviews. We propose a new
framework that utilizes the ranks of pairwise distances among observations and
identifies a common pattern under moderate to high dimensions that has been
overlooked before. The proposed method exhibits superior classification power
over existing methods under a variety of scenarios. Furthermore, the proposed
method can be applied to non-Euclidean data objects, such as network data. We
illustrate the method through an analysis of Neuropixels data where neurons are
classified based on their firing activities. Additionally, we explore a related
approach that is simpler to understand and investigates key quantities that
play essential roles in our novel approach.
