Sequential change-point detection based on direct density-ratio estimation. Kawahara, Y. & Sugiyama, M. Statistical Analysis and Data Mining: The ASA Data Science Journal, 5(2):114–127, 2012. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sam.10124![Sequential change-point detection based on direct density-ratio estimation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel nonparametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimate the ratio of probability densities, not the probability densities themselves. This formulation allows us to avoid nonparametric density estimation, which is known to be a difficult problem. We provide a change-point detection algorithm based on direct density-ratio estimation that can be computed very efficiently in an online manner. The usefulness of the proposed method is demonstrated through experiments using artificial and real-world datasets. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2011
@article{kawahara_sequential_2012,
title = {Sequential change-point detection based on direct density-ratio estimation},
volume = {5},
copyright = {Copyright © 2011 Wiley Periodicals, Inc.},
issn = {1932-1872},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.10124},
doi = {10.1002/sam.10124},
abstract = {Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel nonparametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimate the ratio of probability densities, not the probability densities themselves. This formulation allows us to avoid nonparametric density estimation, which is known to be a difficult problem. We provide a change-point detection algorithm based on direct density-ratio estimation that can be computed very efficiently in an online manner. The usefulness of the proposed method is demonstrated through experiments using artificial and real-world datasets. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2011},
language = {en},
number = {2},
urldate = {2020-10-04},
journal = {Statistical Analysis and Data Mining: The ASA Data Science Journal},
author = {Kawahara, Yoshinobu and Sugiyama, Masashi},
year = {2012},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sam.10124},
keywords = {change-point detection, density-ratio estimation, time-series data},
pages = {114--127},
}
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