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2009
SIAM

Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation.

10 years 4 months ago
Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation.
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 non-parametric 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 non-parametric 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 datasets.
Masashi Sugiyama, Yoshinobu Kawahara
Added 07 Mar 2010
Updated 07 Mar 2010
Type Conference
Year 2009
Where SDM
Authors Masashi Sugiyama, Yoshinobu Kawahara
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