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
    PublisherSpringer Verlag
    Pages11-19
    Number of pages9
    Volume9948 LNCS
    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)03029743
    ISSN (Electronic)16113349

    Other

    Other23rd International Conference on Neural Information Processing, ICONIP 2016
    CountryJapan
    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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  • Cite this

    Hino, H., Akaho, S., & Murata, N. (2016). An entropy estimator based on polynomial regression with poisson error structure. In Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings (Vol. 9948 LNCS, pp. 11-19). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9948 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46672-9_2