Neural implementation of the JADE-algorithm

  • Christian Ziegaus
  • Elmar W. Lang
Engeneering Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1607)

Abstract

The Joint Approaximative Diagonalization of Eigenmatrices (JADE)-algorithm [6] is an algebraic approach for Indenpendent Component Analysis (ICA), a recent data analysis technique. The basic assumption of ICA is a linear superposition model where unknown source signals are mixed together by a mixing matrix. The aims is to recover the sources respectively the mixing matrix based upon the mixtures with only minimum or no knowledge about the sources. We will present a neural extension of the JADE-algorithm, discuss the properties of this new extension and apply it to an arbitrary mixture of real-world images.

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Copyright information

© Springer-Verlag 1999

Authors and Affiliations

  • Christian Ziegaus
    • 1
  • Elmar W. Lang
    • 1
  1. 1.Institute of BiophysicsUniversity of RegensburgRegensburgGermany

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