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
Conference paper

DOI: 10.1007/978-3-540-30110-3_125

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3195)
Cite this paper as:
Gruber P. et al. (2004) Denoising Using Local ICA and a Generalized Eigendecomposition with Time-Delayed Signals. In: Puntonet C.G., Prieto A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg

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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