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Nonparametrick-nearest-neighbor entropy estimator

Damiano Lombardi, Sanjay Pant Orcid Logo

Physical Review E, Volume: 93, Issue: 1

Swansea University Author: Sanjay Pant Orcid Logo

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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...

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Published in: Physical Review E
ISSN: 2470-0045 2470-0053
Published: 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa34501
first_indexed 2017-06-27T20:09:31Z
last_indexed 2018-02-09T05:24:42Z
id cronfa34501
recordtype SURis
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spelling 2017-07-05T13:40:41.4420111 v2 34501 2017-06-27 Nonparametrick-nearest-neighbor entropy estimator 43b388e955511a9d1b86b863c2018a9f 0000-0002-2081-308X Sanjay Pant Sanjay Pant true false 2017-06-27 ACEM 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. Journal Article Physical Review E 93 1 2470-0045 2470-0053 21 1 2016 2016-01-21 10.1103/PhysRevE.93.013310 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2017-07-05T13:40:41.4420111 2017-06-27T16:23:59.3397383 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Damiano Lombardi 1 Sanjay Pant 0000-0002-2081-308X 2
title Nonparametrick-nearest-neighbor entropy estimator
spellingShingle Nonparametrick-nearest-neighbor entropy estimator
Sanjay Pant
title_short Nonparametrick-nearest-neighbor entropy estimator
title_full Nonparametrick-nearest-neighbor entropy estimator
title_fullStr Nonparametrick-nearest-neighbor entropy estimator
title_full_unstemmed Nonparametrick-nearest-neighbor entropy estimator
title_sort Nonparametrick-nearest-neighbor entropy estimator
author_id_str_mv 43b388e955511a9d1b86b863c2018a9f
author_id_fullname_str_mv 43b388e955511a9d1b86b863c2018a9f_***_Sanjay Pant
author Sanjay Pant
author2 Damiano Lombardi
Sanjay Pant
format Journal article
container_title Physical Review E
container_volume 93
container_issue 1
publishDate 2016
institution Swansea University
issn 2470-0045
2470-0053
doi_str_mv 10.1103/PhysRevE.93.013310
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 0
active_str 0
description 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.
published_date 2016-01-21T04:13:53Z
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score 11.096117

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