Go to content
UR Home

KPCA denoising and the pre-image problem revisited

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

Full text not available from this repository.

at publisher (via DOI)


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 ...


Export bibliographical data

Item type:Article
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
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
Item ID:16924
Owner only: item control page
  1. Homepage UR

University Library

Publication Server


Publishing: oa@ur.de

Dissertations: dissertationen@ur.de

Research data: daten@ur.de

Contact persons