Change-point detection in time-series data by relative density-ratio estimation
TL;DR: In this paper, the relative Pearson divergence is used as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation, which can detect abrupt property changes lying behind time-series data.
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About: This article is published in Neural Networks. The article was published on 01 Jul 2013. and is currently open access. The article focuses on the topics: Change detection & Divergence (statistics).
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