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. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Liu, S., Yamada, M., Collier, N., & Sugiyama, M. (2012). Change-point detection in time-series data by relative density-ratio estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7626 LNCS, pp. 363–372). https://doi.org/10.1007/978-3-642-34166-3_40
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