Feature Extraction for Change-Point Detection Using Stationary Subspace Analysis
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 ve...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2012-04, Vol.23 (4), p.631-643 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Blythe, D. A. J. von Bunau, P. Meinecke, F. C. Muller, K-R |
| description | 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. |
| doi_str_mv | 10.1109/TNNLS.2012.2185811 |
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| subjects | Accuracy Applied sciences Change-point detection Computer science control theory systems Computer simulation Control theory. Systems Covariance matrix Data models Density Detection algorithms Exact sciences and technology Feature extraction high-dimensional data Inference from stochastic processes time series analysis Mathematical analysis Mathematics Modelling and identification Monitoring Neural networks Probability and statistics Sciences and techniques of general use segmentation stationarity Statistics Subspaces Time series Time series analysis |
| title | Feature Extraction for Change-Point Detection Using Stationary Subspace Analysis |
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