Optimizing of recurrence plots for noise reduction

We propose a way to automatically detect the best neighborhood size for a local projective noise reduction filter, where a typical problem is the proper identification of the noise level. Here we make use of concepts from the recurrence quantification analysis in order to adaptively tune the filter...

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Bibliographic Details
Published in:Physical review. E, Statistical, nonlinear, and soft matter physics Statistical, nonlinear, and soft matter physics, 2002-02, Vol.65 (2 Pt 1), p.021102-021102
Main Authors: Matassini, Lorenzo, Kantz, Holger, Hołyst, Janusz, Hegger, Rainer
Format: Article
Language:English
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Summary:We propose a way to automatically detect the best neighborhood size for a local projective noise reduction filter, where a typical problem is the proper identification of the noise level. Here we make use of concepts from the recurrence quantification analysis in order to adaptively tune the filter along the incoming time series. We define an index, to be computed via recurrence plots, whose minimum gives a clear indication of the best size of the neighborhood in the embedding space. Comparison of the local projective noise reduction filter using this optimization scheme with the state of the art is also provided.
ISSN:1539-3755
DOI:10.1103/PhysRevE.65.021102