Kernel principal components (KPC) and kernel discriminant coordinates (KDC), which are the extensions of principal components and discriminant coordinates, respectively, from a linear domain to a nonlinear domain via the kernel trick, are two very popular nonlinear feature extraction methods. The kernel discriminant coordinates space has proven to be a very powerful space for pattern recognition. However, further study shows that there are still drawbacks in this method. To improve the performance of pattern recognition, we propose a new learning algorithm combining the advantages of KPC and KDC
@article{bwmeta1.element.doi-10_2478_bile-2014-0005,
author = {Karol Der\k egowski and Miros\l aw Krzy\'sko},
title = {A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components},
journal = {Biometrical Letters},
volume = {51},
year = {2014},
pages = {57-73},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.doi-10_2478_bile-2014-0005}
}
Karol Deręgowski; Mirosław Krzyśko. A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components. Biometrical Letters, Tome 51 (2014) pp. 57-73. http://gdmltest.u-ga.fr/item/bwmeta1.element.doi-10_2478_bile-2014-0005/
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