Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Noh, Yung-Kyun | - |
dc.contributor.author | Sugiyama, Masashi | - |
dc.contributor.author | Liu, Song | - |
dc.contributor.author | du Plessis, Marthinus C. | - |
dc.contributor.author | Park, Frank Chongwoo | - |
dc.contributor.author | Lee, Daniel D. | - |
dc.date.accessioned | 2022-07-12T17:11:35Z | - |
dc.date.available | 2022-07-12T17:11:35Z | - |
dc.date.created | 2021-05-14 | - |
dc.date.issued | 2018-01 | - |
dc.identifier.issn | 0899-7667 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/150666 | - |
dc.description.abstract | Nearest-neighbor estimators for the Kullback-Leiber (KL) divergence that are asymptotically unbiased have recently been proposed and demonstrated in a number of applications. However, with a small number of samples, nonparametric methods typically suffer from large estimation bias due to the nonlocality of information derived from nearest-neighbor statistics. In this letter, we show that this estimation bias can be mitigated by modifying the metric function, and we propose a novel method for learning a locally optimal Mahalanobis distance function from parametric generative models of the underlying density distributions. Using both simulations and experiments on a variety of data sets, we demonstrate that this interplay between approximate generative models and nonparametric techniques can significantly improve the accuracy of nearest-neighbor-based estimation of the KL divergence. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MIT PRESS | - |
dc.title | Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Noh, Yung-Kyun | - |
dc.identifier.doi | 10.1162/neco_a_01092 | - |
dc.identifier.scopusid | 2-s2.0-85048930046 | - |
dc.identifier.wosid | 000435657600006 | - |
dc.identifier.bibliographicCitation | NEURAL COMPUTATION, v.30, no.7, pp.1930 - 1960 | - |
dc.relation.isPartOf | NEURAL COMPUTATION | - |
dc.citation.title | NEURAL COMPUTATION | - |
dc.citation.volume | 30 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 1930 | - |
dc.citation.endPage | 1960 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science, Artificial IntelligenceNeurosciences | - |
dc.relation.journalWebOfScienceCategory | Computer ScienceNeurosciences & Neurology | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
dc.subject.keywordPlus | GENE-EXPRESSION | - |
dc.subject.keywordPlus | INFORMATION | - |
dc.subject.keywordPlus | RELEVANCE | - |
dc.identifier.url | https://direct.mit.edu/neco/article-abstract/30/7/1930/8407/Bias-Reduction-and-Metric-Learning-for-Nearest?redirectedFrom=fulltext | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.