Change-point detection in a sequence of bags-of-data

Kensuke Koshijima, Hideitsu Hino, Noboru Murata

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

    1 Citation (Scopus)

    Abstract

    In this paper, the limitation that is prominent in most existing works of change-point detection methods is addressed by proposing a nonparametric, computationally efficient method. The limitation is that most works assume that each data point observed at each time step is a single multi-dimensional vector. However, there are many situations where this does not hold. Therefore, a setting where each observation is a collection of random variables, which we call a bag of data, is considered.

    Original languageEnglish
    Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1560-1561
    Number of pages2
    ISBN (Electronic)9781509020195
    DOIs
    Publication statusPublished - 2016 Jun 22
    Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
    Duration: 2016 May 162016 May 20

    Other

    Other32nd IEEE International Conference on Data Engineering, ICDE 2016
    CountryFinland
    CityHelsinki
    Period16/5/1616/5/20

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    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computational Theory and Mathematics
    • Computer Graphics and Computer-Aided Design
    • Computer Networks and Communications
    • Information Systems
    • Information Systems and Management

    Cite this

    Koshijima, K., Hino, H., & Murata, N. (2016). Change-point detection in a sequence of bags-of-data. In 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 (pp. 1560-1561). [7498425] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2016.7498425