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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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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|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang|
|Keywords:||Kernel principal component analysis (KPCA); Pre-image; Time series analysis; Denoising|
|Dewey Decimal Classification:||500 Science > 570 Life sciences|
|Created at the University of Regensburg:||Unknown|
|Deposited on:||05 Oct 2010 06:30|
|Last modified:||05 Oct 2010 06:30|