Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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 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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The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
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The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph