A Dimension-Independent discriminant between distributions
Information Theory
2018-02-14 v1 math.IT
Statistics Theory
Statistics Theory
Abstract
Henze-Penrose divergence is a non-parametric divergence measure that can be used to estimate a bound on the Bayes error in a binary classification problem. In this paper, we show that a cross-match statistic based on optimal weighted matching can be used to directly estimate Henze-Penrose divergence. Unlike an earlier approach based on the Friedman-Rafsky minimal spanning tree statistic, the proposed method is dimension-independent. The new approach is evaluated using simulation and applied to real datasets to obtain Bayes error estimates.
Cite
@article{arxiv.1802.04497,
title = {A Dimension-Independent discriminant between distributions},
author = {Salimeh Yasaei Sekeh and Brandon Oselio and Alfred O. Hero},
journal= {arXiv preprint arXiv:1802.04497},
year = {2018}
}
Comments
5 pages, 5 figures, ICASSP 2018