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

arXiv: https://arxiv.org/abs/2306.13985 · HTML: https://mlsanity.com/paper/2306.13985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [The classification for High-dimension low-sample size data](https://mlsanity.com/paper/2006.13018/) (2020)
- [On Generalizations of Some Distance Based Classifiers for HDLSS Data](https://mlsanity.com/paper/1902.03295/) (2019)
- [On Exact Feature Screening in Ultrahigh-dimensional Binary Classification](https://mlsanity.com/paper/2205.03831/) (2022)
- [Population-Guided Large Margin Classifier for High-Dimension Low -Sample-Size Problems](https://mlsanity.com/paper/1901.01377/) (2019)

## Abstract (arXiv)

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.