Application of Kernel Principal Components Analysis to Pattern Recognitions

Kosuke Sohara; Manabu Kotani

doi:10.11499/sicep.2002.0.161.0

SICE Annual Conference 2002

DOI https://doi.org/10.11499/sicep.2002.0.161.0

Conference information

Host: The Society of Instrument and Control Engineers

Co-host: IEEE/Industiral Electronic Society, IEEE/Robotics and Automation Society, IEEE/Control System Society

Application of Kernel Principal Components Analysis to Pattern Recognitions

Kosuke Sohara, Manabu Kotani

Author information

Keywords: Kernel PCA, Support Vector Machine, Kernel method

CONFERENCE PROCEEDINGS FREE ACCESS

Pages 161

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Abstract

Kernel Principal Component Analysis (Kernel PCA) is one of the methods to perform PCA in high dimensional space. The purpose of this paper is to examine what components are obtained by Kernel PCA and evaluate effectiveness of the components as feature. Simulation’s results show that Kernel PCA can get superior performance to PCA.

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