Non-parametric e-mixture of density functions

Hideitsu Hino*, Ken Takano, Shotaro Akaho, Noboru Murata

*Corresponding author for this work

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

    1 Citation (Scopus)


    Mixture modeling is one of the simplest ways to represent complicated probability density functions, and to integrate information from different sources. There are two typical mixtures in the context of information geometry, the m-and e-mixtures. This paper proposes a novel framework of non-parametric e-mixture modeling by using a simple estimation algorithm based on geometrical insights into the characteristics of the e-mixture. An experimental result supports the proposed framework.

    Original languageEnglish
    Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
    PublisherSpringer Verlag
    Number of pages8
    Volume9948 LNCS
    ISBN (Print)9783319466712
    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


    Other23rd International Conference on Neural Information Processing, ICONIP 2016


    • Information geometry
    • Mixture model
    • Non-parametric method

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science


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