Kernel Entropy Component Analysis Pre-images for Pattern Denoising

  • Robert Jenssen
  • Ola Storås
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)


The recently proposed kernel entropy component analysis (kernel ECA) technique may produce strikingly different spectral data sets than kernel PCA for a wide range of kernel sizes. In this paper, we investigate the use of kernel ECA as a component in a denoising technique previously developed for kernel PCA. The method is based on mapping noisy data to a kernel feature space, for then to denoise by projecting onto a kernel ECA subspace. The denoised data in the input space is obtained by computing pre-images of kernel ECA denoised patterns. The denoising results are in several cases improved.


Input Space Kernel Matrix Neural Information Processing System Kernel Size Kernel Principal Component Analysis 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Robert Jenssen
    • 1
  • Ola Storås
    • 1
  1. 1.Department of Physics and TechnologyUniversity of TromsøTromsøNorway

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