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abstract: We present and test an extension of slow feature analysis as a novel approach to nonlinear blind source separation. The algorithm relies on temporal correlations and iteratively reconstructs a set of statistically independent sources from arbitrary nonlinear instantaneous mixtures. Simulations show that it is able to invert a complicated nonlinear mixture of two audio signals with a reliability of more than $90$\%. The algorithm is based on a  mathematical analysis of slow feature analysis for the case of input data that are generated from statistically independent sources.
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creators_id:
  - henning.sprekeler@epfl.ch
  - tiziano.zito@bccn-berlin.de
  - l.wiskott@biologie.hu-berlin.de
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  - family: Sprekeler
    given: Henning
    honourific: Dr.
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  - family: Zito
    given: Tiziano
    honourific: ''
    lineage: ''
  - family: Wiskott
    given: Laurenz
    honourific: Dr.
    lineage: ''
date: 2010-10
date_type: submitted
datestamp: 2010-10-18 11:03:37
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dir: disk0/00/00/70/56
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keywords: 'slow feature analysis, nonlinear blind source separation, independent component analysis'
lastmod: 2011-03-11 08:57:45
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rev_number: 40
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status_changed: 2010-10-18 11:03:37
subjects:
  - comp-sci-mach-learn
succeeds: 7055
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title: An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation
type: preprint
userid: 6764
volume: ~
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