Chapter

Independent Component Analysis and Blind Signal Separation

Volume 3195 of the series Lecture Notes in Computer Science pp 993-1000

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

  • Peter GruberAffiliated withInstitute of Biophysics, University of Regensburg
  • , Kurt StadlthannerAffiliated withInstitute of Biophysics, University of Regensburg
  • , Ana Maria ToméAffiliated withDept. de Electrónica e Telecomunicações/IEETA, Universidade de Aveiro
  • , Ana R. TeixeiraAffiliated withDept. de Electrónica e Telecomunicações/IEETA, Universidade de Aveiro
  • , Fabian J. TheisAffiliated withInstitute of Biophysics, University of Regensburg
  • , Carlos G. PuntonetAffiliated withDep. Arquitectura y Tecnologia de Computadores, Universidad de Granada
  • , Elmar W. LangAffiliated withInstitute of Biophysics, University of Regensburg

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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.