Sequential change-point detection based on direct density-ratio estimation

Authors:
Yoshinobu Kawahara;Masashi Sugiyama
Affiliations:
The Institute of Scientific and Industrial Research (ISIR), Osaka University, Osaka, Japan;Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan
Venue:
Statistical Analysis and Data Mining
Year:
2012

Citing 18
Cited 5

Detection of abrupt changes: theory and application

Detection of abrupt changes: theory and application
Event detection from time series data

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised Profiling for Identifying Superimposed Fraud

PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A unifying framework for detecting outliers and change points from non-stationary time series data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches

Signal Processing
Novelty detection: a review—part 2: neural network based approaches

Signal Processing
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms

Data Mining and Knowledge Discovery
Eigenspace-based anomaly detection in computer systems

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An approach to spacecraft anomaly detection problem using kernel feature space

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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
Detecting change in data streams

VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Change-Point Detection in Time-Series Data Based on Subspace Identification

ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Dimensionality reduction for density ratio estimation in high-dimensional spaces

Neural Networks
Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search

Neural Networks
An online kernel change detection algorithm

IEEE Transactions on Signal Processing - Part II
Online learning with kernels

IEEE Transactions on Signal Processing

Change-point detection in time-series data by relative density-ratio estimation

SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Computational complexity of kernel-based density-ratio estimation: a condition number analysis

Machine Learning
Change-point detection in time-series data by relative density-ratio estimation

Neural Networks
Density-difference estimation

Neural Computation
Coding of non-stationary sources as a foundation for detecting change points and outliers in binary time-series

AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134

Quantified Score

Hi-index	0.00

Visualization

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.