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 crossmatch 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.
In REF , the authors showed that a cross match statistic based on optimal weighted matching can also be used to directly estimate the HP divergence.
A Dimension-Independent Discriminant Between Distributions
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science Mathematics