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KPCA denoising and the pre-image problem revisited

Texeira, A. R. and Tomé, A. M. and Stadlthanner, K. and Lang, Elmar (2008) KPCA denoising and the pre-image problem revisited. Digital Signal Processing 18 (4), pp. 568-580.

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Abstract

Kernel principal component analysis (KPCA) is widely used in classification, feature extraction and denoising applications. In the latter it is unavoidable to deal with the pre-image problem which constitutes the most complex step in the whole processing chain. One of the methods to tackle this problem is an iterative solution based on a fixed-point algorithm. An alternative strategy considers an ...

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Item Type:Article
Date:2008
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
ValueType
10.1016/j.dsp.2007.08.001DOI
Keywords:Kernel principal component analysis (KPCA); Pre-image; Time series analysis; Denoising
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Owner: Gertraud Kellers
Deposited On:05 Oct 2010 06:30
Last Modified:05 Oct 2010 06:30
Item ID:16924
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