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.
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Liu, S., Yamada, M., Collier, N., Sugiyama, M. (2012). Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_40
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DOI: https://doi.org/10.1007/978-3-642-34166-3_40
Publisher Name: Springer, Berlin, Heidelberg
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