Paper Info
Title
Sequential change-point detection based on direct density-ratio estimation
Abstract
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 nonparametric 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 nonparametric 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-world datasets. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2011 © 2012 Wiley Periodicals, Inc.
Year
DOI
Venue
2012
10.1002/sam.10124
Statistical Analysis and Data Mining
Keywords
Field
DocType
inc. statistical analysis,data mining,difficult problem,wiley periodicals,change-point detection,probability density,sequence data,sequential change-point detection,direct density-ratio estimation,change-point detection algorithm,time-series data change,probability distribution,change point detection,ratio estimator,time series data
Density estimation,Data mining,Time series,Change detection,Computer science,Nonparametric statistics,Probability distribution,Data sequences,Density ratio estimation,Statistical analysis
Journal
Volume
Issue
Citations 
5
2
36
PageRank 
References 
Authors
1.47
26
2
Name
Order
Citations
PageRank
Kawahara, Yoshinobu131731.30
Masashi Sugiyama23353264.24