Denoising using local projective subspace methods

  • Authors:

  • P. Gruber;K. Stadlthanner;M. Böhm;F. J. Theis;E. W. Lang;A. M. Tomé;A. R. Teixeira;C. G. Puntonet;J. M. Gorriz Saéz

  • Affiliations:

  • Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, 93040 Regensburg, Germany;Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, 93040 Regensburg, Germany;Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, 93040 Regensburg, Germany;Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, 93040 Regensburg, Germany;Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, 93040 Regensburg, Germany;Departamento de Electrónica e Telecomunicaçíes/IEETA,Universidade de Aveiro, 3810 Aveiro, Portugal;Departamento de Electrónica e Telecomunicaçíes/IEETA,Universidade de Aveiro, 3810 Aveiro, Portugal;Departamento Arqitectura y Técnologia de Computadores,Universidad de Granada, 18371 Granada, Spain;Departamento Arqitectura y Técnologia de Computadores,Universidad de Granada, 18371 Granada, Spain

  • Venue:
  • Neurocomputing
  • Year:
  • 2006

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Abstract

In this paper we present denoising algorithms for enhancing noisy signals based on Local ICA (LICA), Delayed AMUSE (dAMUSE) and Kernel PCA (KPCA). The algorithm LICA relies on applying ICA locally to clusters of signals embedded in a high-dimensional feature space of delayed coordinates. The components resembling the signals can be detected by various criteria like estimators of kurtosis or the variance of autocorrelations depending on the statistical nature of the signal. The algorithm proposed can be applied favorably to the problem of denoising multi-dimensional data. Another projective subspace denoising method using delayed coordinates has been proposed recently with the algorithm dAMUSE. It combines the solution of blind source separation problems with denoising efforts in an elegant way and proofs to be very efficient and fast. Finally, KPCA represents a non-linear projective subspace method that is well suited for denoising also. Besides illustrative applications to toy examples and images, we provide an application of all algorithms considered to the analysis of protein NMR spectra.