High-Dimensional Density Estimation for Data Mining Tasks

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    4 Citations (Scopus)

    Abstract

    Consider a problem of estimating an unknown high dimensional density whose support lies on unknown low-dimensional data manifold. This problem arises in many data mining tasks, and the paper proposes a new geometrically motivated solution for the problem in manifold learning framework, including an estimation of an unknown support of the density. Firstly, tangent bundle manifold learning problem is solved resulting in transforming high dimensional data into their low-dimensional features and estimating the Riemannian tensor on the Data manifold. After that, an unknown density of the constructed features is estimated with the use of appropriate kernel approach. Finally, with the use of estimated Riemannian tensor, the final estimator of the initial density is constructed.

    Original languageEnglish
    Title of host publicationProceeding - 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
    EditorsRaju Gottumukkala, George Karypis, Vijay Raghavan, Xindong Wu, Lucio Miele, Srinivas Aluru, Xia Ning, Guozhu Dong
    PublisherIEEE Computer Society
    Pages523-530
    Number of pages8
    ISBN (Electronic)9781538614808
    DOIs
    Publication statusPublished - 15 Dec 2017
    Event17th IEEE International Conference on Data Mining Workshops, ICDMW 2017 - New Orleans, United States
    Duration: 18 Nov 201721 Nov 2017

    Publication series

    NameIEEE International Conference on Data Mining Workshops, ICDMW
    Volume2017-November
    ISSN (Print)2375-9232
    ISSN (Electronic)2375-9259

    Conference

    Conference17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
    Country/TerritoryUnited States
    CityNew Orleans
    Period18/11/1721/11/17

    Keywords

    • Density on feature space estimation
    • Density on manifold estimation
    • Dimensionality Reduction
    • High-dimensional manifold valued data
    • Manifold learning

    Fingerprint

    Dive into the research topics of 'High-Dimensional Density Estimation for Data Mining Tasks'. Together they form a unique fingerprint.

    Cite this