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@article{liu_change-point_2013,
	title = {Change-point detection in time-series data by relative density-ratio estimation},
	volume = {43},
	issn = {0893-6080},
	url = {https://www.sciencedirect.com/science/article/pii/S0893608013000270},
	doi = {10.1016/j.neunet.2013.01.012},
	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.},
	language = {en},
	urldate = {2022-08-25},
	journal = {Neural Networks},
	author = {Liu, Song and Yamada, Makoto and Collier, Nigel and Sugiyama, Masashi},
	month = jul,
	year = {2013},
	keywords = {Change-point detection, Distribution comparison, Kernel methods, Relative density-ratio estimation, Time-series data},
	pages = {72--83},
}