Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Machine Learning
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Estimating the Support of a High-Dimensional Distribution
Neural Computation
A Unifying Framework for Detecting Outliers and Change Points from Time Series
IEEE Transactions on Knowledge and Data Engineering
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Change-Point Detection in Time-Series Data Based on Subspace Identification
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Sequential Bayesian prediction in the presence of changepoints
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A Least-squares Approach to Direct Importance Estimation
The Journal of Machine Learning Research
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Streaming first story detection with application to Twitter
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Estimating divergence functionals and the likelihood ratio by convex risk minimization
IEEE Transactions on Information Theory
Statistical outlier detection using direct density ratio estimation
Knowledge and Information Systems
Neural Networks
Density Ratio Estimation in Machine Learning
Density Ratio Estimation in Machine Learning
An online kernel change detection algorithm
IEEE Transactions on Signal Processing - Part II
Sequential change-point detection based on direct density-ratio estimation
Statistical Analysis and Data Mining
-Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio Models
IEEE Transactions on Information Theory
Neural Computation
Change-point detection with feature selection in high-dimensional time-series data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.