Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation (情報論的学習理論と機械学習)

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概要

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• 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 artificial and real-world datasets including human-activity sensing, speeches, and Twitter archives, we demonstrate the usefulness of the proposed method.

• 2011-11-02

著者

• Yamada Makoto

  Department Of Chemistry And Biomolecular Science Toho University

• Sugiyama Masashi

  Department Of Chemistry Faculty Of Science Tokyo University Of Science

• Liu Song

  Department Of Computer Science Tokyo Institute Of Technology

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