PROJECTIVE NOISE CLEANING WITH DYNAMIC NEIGHBORHOOD SELECTION

A. Kern (), W.-H. Steeb and R. Stoop
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A. Kern: Institut für Neuroinformatik, University of Zurich/ETH Zurich, CH-8057, Zürich
W.-H. Steeb: Institut für Neuroinformatik, University of Zurich/ETH Zurich, CH-8057, Zürich;
R. Stoop: Institut für Neuroinformatik, University of Zurich/ETH Zurich, CH-8057, Zürich

International Journal of Modern Physics C (IJMPC), 2000, vol. 11, issue 01, 125-146

Abstract: In recent years, several methods of noise cleaning have been devised, of which projective methods have been particularly effective. In our paper, we explain in detail why orthogonal projections are nonoptimal and how the nonorthogonal projections suggested by Grassbergeret al., naturally emerge from the SVD method. We show that this approach when combined with a dynamic neighborhood selection yields optimal results of noise cleaning.

Keywords: Noise Cleaning; Projective Methods; Singular Value Decomposition; Neighborhood Search (search for similar items in EconPapers)
Date: 2000
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DOI: 10.1142/S0129183100000110

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