Feature Extraction for Change-Point Detection using Stationary Subspace Analysis

Details Feature Extraction for Change-Point Detection using Stationary Subspace Analysis Feature Extraction for Change-Point Detection using Stationary Subspace Analysis

2011-08-11

arxiv.org/abs/1108.2486v1

Computer Science

Learning

24 pages, 20 figures, journal preprint

Scientific paper

10.1109/TNNLS.2012.2185811

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 non-stationary 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.

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