Feature extraction for change-point detection using stationary subspace analysis

Duncan A.J. Blythe, Paul Von Bunau, Frank C. Meinecke, Klaus Robert Muller

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


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.

Original languageEnglish
Article number6151166
Pages (from-to)631-643
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number4
Publication statusPublished - 2012
Externally publishedYes


  • Change-point detection
  • feature extraction
  • high-dimensional data
  • segmentation
  • stationarity
  • time-series analysis

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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