Kernel Entropy Component Analysis Pre-images for Pattern Denoising

Authors:
Robert Jenssen;Ola Storås
Affiliations:
Department of Physics and Technology, University of Tromsø, Tromsø, Norway 9037;Department of Physics and Technology, University of Tromsø, Tromsø, Norway 9037
Venue:
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Year:
2009

Citing 5
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Abstract

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.