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A Dimension-Independent Discriminant between Distributions
- Citation Author(s):
- Submitted by:
- Brandon Oselio
- Last updated:
- 19 April 2018 - 12:32pm
- Document Type:
- Poster
- Document Year:
- 2018
- Event:
- Presenters:
- Brandon Oselio
- Paper Code:
- SPTM-P7
- Categories:
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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.
icassp2018.pdf
icassp2018.pdf (667)