Denoising Using Local ICA and a Generalized Eigendecomposition with Time-Delayed Signals

  • Peter Gruber
  • Kurt Stadlthanner
  • Ana Maria Tomé
  • Ana R. Teixeira
  • Fabian J. Theis
  • Carlos G. Puntonet
  • Elmar W. Lang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3195)

Abstract

We present denoising algorithms based on either local independent component analysis (ICA) and a minimum description length (MDL) estimator or a generalized eigenvalue decomposition (GEVD) using a matrix pencil of time-delayed signals. Both methods are applied to signals embedded in delayed coordinates in a high-dim feature space Ω and denoising is achieved by projecting onto a lower dimensional signal subspace. We discuss the algorithms and provide applications to the analysis of 2D NOESY protein NMR spectra.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Peter Gruber
    • 1
  • Kurt Stadlthanner
    • 1
  • Ana Maria Tomé
    • 2
  • Ana R. Teixeira
    • 2
  • Fabian J. Theis
    • 1
  • Carlos G. Puntonet
    • 3
  • Elmar W. Lang
    • 1
  1. 1.Institute of BiophysicsUniversity of RegensburgRegensburgGermany
  2. 2.Dept. de Electrónica e Telecomunicações/IEETAUniversidade de AveiroAveiroPortugal
  3. 3.Dep. Arquitectura y Tecnologia de ComputadoresUniversidad de GranadaGranadaSpain

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