<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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 </p></abstract>

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

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

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

Biometrical Letters

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Kernel principal components (KPC) and kernel discriminant coordinates (KDC), which are the extensions of principal components and discriminant coordinates,...

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

Online veröffentlicht: 06. Juni 2014

Seitenbereich: 57 - 73

DOI: https://doi.org/10.2478/bile-2014-0005

Schlüsselwörter
kernel principal components, kernel discriminant coordinates

© by Mirosław Krzyśko

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Online veröffentlicht: 06. Juni 2014

Seitenbereich: 57 - 73

DOI: https://doi.org/10.2478/bile-2014-0005

Schlüsselwörterkernel principal components, kernel discriminant coordinates

© by Mirosław Krzyśko

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Schlüsselwörter
kernel principal components, kernel discriminant coordinates