title: Change-point detection in time-series data by relative density-ratio estimation.
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 based on non-parametric divergence estimation between time-series samples from 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, speech, and Twitter messages, we demonstrate the usefulness of the proposed method.
authors.affiliation: Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan. song@sg.cs.titech.ac.jp
authors.forename: Song
authors.initials: S
authors.lastname: Liu
authors.forename: Makoto
authors.initials: M
authors.lastname: Yamada
authors.forename: Nigel
authors.initials: N
authors.lastname: Collier
authors.forename: Masashi
authors.initials: M
authors.lastname: Sugiyama
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D012189:Retrospective Studies; D013046:Specific Gravity; D013997:Time Factors