Open Access
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation (情報論的学習理論と機械学習)
Song Liu,Makoto Yamada,Masashi Sugiyama +2 more
- Vol. 111, Iss: 275, pp 187-198
TLDR
This paper presents a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments that is accurately and efficiently estimated by a method of direct density-ratio estimation.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.read more
Citations
More filters
Posted Content
Concrete Problems in AI Safety
TL;DR: A list of five practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function, an objective function that is too expensive to evaluate frequently, or undesirable behavior during the learning process, are presented.
Journal ArticleDOI
Selective review of offline change point detection methods
TL;DR: In this article, the authors present a selective survey of algorithms for the offline detection of multiple change points in multivariate time series, and a general yet structuring methodological strategy is adopted to organize this vast body of work.
Proceedings ArticleDOI
Generic and Scalable Framework for Automated Time-series Anomaly Detection
TL;DR: A generic and scalable framework for automated anomaly detection on large scale time-series data and the open-sourcing of the data represents the first of its kind effort to establish the standard benchmark for anomaly detection.
Proceedings Article
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
TL;DR: In this article, the residual sum of squares of linear least-squares regression is used to estimate a set of functions that transforms data into a form in which the linear regression fits well.
References
More filters
Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.