Change-point detection in time-series data by relative density-ratio estimation

Authors:
Song Liu;Makoto Yamada;Nigel Collier;Masashi Sugiyama
Affiliations:
Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan;NTT Communication Science Laboratories, Seika-cho, Kyoto, Japan;National Institute of Informatics, Chiyoda-ku, Tokyo, Japan;Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

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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.