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A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components

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Deręgowski K., Krzyśko M.

Biometrical Letters

2014

tom 51

nr 1

57-73

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

Ograniczanie wyników

1 Biometrical Letters

1 Deręgowski K.

1 Krzyśko M.

1 2014

Wyniki wyszukiwania

A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components