Title:

Supervised Kernel Principal Component Analysis by Most Expressive Feature Reordering, Journal of Telecommunications and Information Technology, 2015, nr 2

Creator:

Ślot, Krzysztof ; Żurek, Dominik ; Duch, Piotr ; Adamiak, Krzysztof

Subject and Keywords:

pattern classification ; feature selection ; kernel methods

Description:

The presented paper is concerned with feature space derivation through feature selection. The selection is performed on results of kernel Principal Component Analysis (kPCA) of input data samples. Several criteria that drive feature selection process are introduced and their performance is assessed and compared against the reference approach, which is a combination of kPCA and most expressive feature reordering based on the Fisher linear discriminant criterion. It has been shown that some of the proposed modifications result in generating feature spaces with noticeably better (at the level of approximately 4%) class discrimination properties.

Publisher:

National Institute of Telecommunications

Date:

2015, nr 2

Resource Type:

artykuł

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852

DOI:

10.26636/jtit.2015.2.782

ISSN:

1509-4553

eISSN:

1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności