Change-point detection in time-series data by relative density-ratio estimation
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...
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| Vydané v: | Neural networks Ročník 43; s. 72 - 83 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.07.2013
Elsevier |
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| ISSN: | 0893-6080, 1879-2782, 1879-2782 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Sugiyama, Masashi Collier, Nigel Yamada, Makoto Liu, Song |
| Author_xml | – sequence: 1 givenname: Song surname: Liu fullname: Liu, Song email: song@sg.cs.titech.ac.jp organization: Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan – sequence: 2 givenname: Makoto surname: Yamada fullname: Yamada, Makoto email: yamada.makoto@lab.ntt.co.jp organization: NTT Communication Science Laboratories, 2-4, Hikaridai, Seika-cho, Kyoto 619-0237, Japan – sequence: 3 givenname: Nigel surname: Collier fullname: Collier, Nigel email: collier@nii.ac.jp organization: National Institute of Informatics (NII), 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan – sequence: 4 givenname: Masashi surname: Sugiyama fullname: Sugiyama, Masashi email: sugi@cs.titech.ac.jp organization: Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan |
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| Keywords | Change-point detection Kernel methods Time-series data Distribution comparison Relative density-ratio estimation Statistical analysis Divergence Event detection Time series Non parametric estimation Social network Change point Kernel method Lying Human activity Probability density function Web site |
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| Snippet | 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... |
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| SubjectTerms | Algorithms Applied sciences Change-point detection Computer science; control theory; systems Data processing. List processing. Character string processing Distribution comparison Divergence Exact sciences and technology Gene Expression Profiling - methods Humans Inference from stochastic processes; time series analysis Kernel methods Mathematics Memory organisation. Data processing Messages Models, Statistical Neural networks Probability and statistics Relative density-ratio estimation Retrospective Studies Samples Sciences and techniques of general use Segments Software Specific Gravity Speech Statistical analysis Statistics Time Factors Time-series data |
| Title | Change-point detection in time-series data by relative density-ratio estimation |
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