Journal
NEURAL NETWORKS
Volume 43, Issue -, Pages 72-83Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2013.01.012
Keywords
Change-point detection; Distribution comparison; Relative density-ratio estimation; Kernel methods; Time-series data
Funding
- NII internship fund
- JST PRESTO program
- NII Grand Challenge project fund
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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 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. (c) 2013 Elsevier Ltd. All rights reserved.
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