Journal article 917 views
Nonparametrick-nearest-neighbor entropy estimator
Damiano Lombardi,
Sanjay Pant 
Physical Review E, Volume: 93, Issue: 1
Swansea University Author:
Sanjay Pant
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1103/PhysRevE.93.013310
Abstract
A nonparametric k-nearest-neighbor-based entropy estimator is proposed. It improves on the classical Kozachenko-Leonenko estimator by considering nonuniform probability densities in the region of k-nearest neighbors around each sample point. It aims to improve the classical estimators in three situa...
| Published in: | Physical Review E |
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| ISSN: | 2470-0045 2470-0053 |
| Published: |
2016
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa34501 |
| Abstract: |
A nonparametric k-nearest-neighbor-based entropy estimator is proposed. It improves on the classical Kozachenko-Leonenko estimator by considering nonuniform probability densities in the region of k-nearest neighbors around each sample point. It aims to improve the classical estimators in three situations: first, when the dimensionality of the random variable is large; second, when near-functional relationships leading to high correlation between components of the random variable are present; and third, when the marginal variances of random variable components vary significantly with respect to each other. Heuristics on the error of the proposed and classical estimators are presented. Finally, the proposed estimator is tested for a variety of distributions in successively increasing dimensions and in the presence of a near-functional relationship. Its performance is compared with a classical estimator, and a significant improvement is demonstrated. |
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| College: |
Faculty of Science and Engineering |
| Issue: |
1 |