  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.
