PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Feature Extraction for Change-Point Detection using Stationary Subspace Analysis
D A J Blythe, Paul Buenau, Frank Meinecke and Klaus-Robert Müller
IEEE Transactions on Neural Networks and Learning Systems 2012.

Abstract

Detecting changes in high-dimensional time series is difficult because it involves the comparison of probability densities that need to be estimated from finite samples. In this paper, we present the first feature extraction method tailored to change-point detection, which is based on an extended version of stationary subspace analysis. We reduce the dimensionality of the data to the most nonstationary directions, which are most informative for detecting state changes in the time series. In extensive simulations on synthetic data, we show that the accuracy of three change-point detection algorithms is significantly increased by a prior feature extraction step. These findings are confirmed in an application to industrial fault monitoring.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9465
Deposited By:Paul Buenau
Deposited On:16 March 2012