An entropy estimator based on polynomial regression with poisson error structure

Hideitsu Hino, Shotaro Akaho, Noboru Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A method for estimating Shannon differential entropy is proposed based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Polynomial regression with Poisson error structure is utilized to estimate the values of density function. The density estimates at every given data points are averaged to obtain entropy estimators. The proposed estimator is shown to perform well through numerical experiments for various probability distributions.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
EditorsSeiichi Ozawa, Kazushi Ikeda, Derong Liu, Akira Hirose, Kenji Doya, Minho Lee
PublisherSpringer Verlag
Pages11-19
Number of pages9
ISBN (Print)9783319466712
DOIs
Publication statusPublished - 2016
Event23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
Duration: 2016 Oct 162016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9948 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on Neural Information Processing, ICONIP 2016
Country/TerritoryJapan
CityKyoto
Period16/10/1616/10/21

Keywords

  • Density estimation
  • Entropy
  • Poisson error structure
  • Regression

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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