Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation

  • Song Liu
  • Makoto Yamada
  • Nigel Collier
  • Masashi Sugiyama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)

Abstract

The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm that is based on non-parametric divergence estimation between two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on real-world human-activity sensing, speech, and Twitter datasets, we demonstrate the usefulness of the proposed method.

Keywords

change-point detection distribution comparison relative density-ratio estimation kernel methods time-series data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Song Liu
    • 1
  • Makoto Yamada
    • 2
  • Nigel Collier
    • 3
  • Masashi Sugiyama
    • 1
  1. 1.Tokyo Institute of TechnologyMeguro-kuJapan
  2. 2.NTT Communication Science LaboratoriesSeika-choJapan
  3. 3.National Institute of InformaticsChiyoda-kuJapan

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