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

• Autorzy: Karol Deręgowski , Mirosław Krzyśko
• Języki publikacji: EN

Abstrakty

EN

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

Słowa kluczowe

EN

kernel principal components; kernel discriminant coordinates

• Wydawca: De Gruyter Open
• Czasopismo: Biometrical Letters
• Rocznik: 2014
• Tom: 51
• Numer: 1
• Strony: 57-73

Daty

wydano

2014-06-01

online

2014-06-06

Twórcy

autor

Karol Deręgowski

• President Stanislaw Wojciechowski Higher Vocational State School in Kalisz, Institute of Management, Nowy Świat 4, 62-800 Kalisz, Poland

autor

Mirosław Krzyśko

• President Stanislaw Wojciechowski Higher Vocational State School in Kalisz, Institute of Management, Nowy Świat 4, 62-800 Kalisz, Poland
• Adam Mickiewicz University, Faculty of Mathematics and Computer Science, Umultowska 87, 61-614 Poznań, Poland

Bibliografia

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Identyfikatory

DOI

10.2478/bile-2014-0005