Change-point detection in time-series data by relative density-ratio estimation
TL;DR: In this paper, the relative Pearson divergence is used as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation, which can detect abrupt property changes lying behind time-series data.
read more
About: This article is published in Neural Networks. The article was published on 01 Jul 2013. and is currently open access. The article focuses on the topics: Change detection & Divergence (statistics).
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Cluster-based stability evaluation in time series data sets
TL;DR: In this paper , the authors present a general evaluation measure that examines clusterings with respect to their temporal stability and thus provides information about the achieved quality of the clustering algorithm, and demonstrate the practicality of their approaches on three real world data sets and one generated data set.
•Proceedings Article
Inertial hidden markov models: modeling change in multivariate time series
George D. Montanez,Saeed Amizadeh,Nikolay Laptev +2 more
- 25 Jan 2015
TL;DR: The regularized methods developed here are able to perfectly characterize change of behavior in the human activity data for roughly half of the real-data test cases, with accuracy of 94% and low variation of information.
Scan B-statistic for kernel change-point detection
TL;DR: This article used kernel-based nonparametric statistics to detect abrupt change-point detection in statistics and machine learning models, which enjoys fewer assents than the traditional stochastic model.
Weakened relationship between tree growth and nitrogen availability due to global CO2 increase and warming in the Taibai Mountain timberline, central China
Lelong Yin,Xiaohong Liu,Xiaomin Zeng,Ziyi Wang,Guobao Xu,Liangju Zhao,Qiangqiang Lu,Lingnan Zhang,Xiaoyu Xing +8 more
TL;DR: Tree growth in the Taibai Mountain timberline is sensitive to temperature and nitrogen availability. Climate warming and rising CO2 concentration have increased tree growth, but decreased nitrogen availability.
Change Point Detection Based on Cluster Transition Distributions
Shoko Takahashi,Kei Takeshita,Kazuhisa Yamagishi,Akihiro Shiozu +3 more
TL;DR: This paper proposes a clustering-based Change Point Detection method for non-stationary time-series, enabling pattern changes detection by tracking cluster transitions and calculating distance between past and current cluster transition distributions.
References
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.
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.
•Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
- 01 Jan 1993
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Statistical learning theory
Vladimir Vapnik
- 01 Jan 1998
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.
30.4K