Extraction of discriminant features based on optimal transformation and cluster centers of kernel space

It has been proved a lot of linear feature extraction methods can be generalized to the nonlinear learning methods by using kernel methods. In this paper, a new nonlinear learning method of optimal transformation and cluster centers (OT-CC) is presented by using kernel technique. It is named as opti...

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Bibliographic Details
Published in2008 IEEE International Conference on Mechatronics and Automation pp. 499 - 504
Main Authors Hongyi Zhang, Xiuwei Wu, Jiexin Pu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2008
Subjects
Algorithm design and analysis
Clustering algorithms
Data mining
Feature extraction
Kernel
Kernel method
Learning systems
optimal cluster centers
optimal transformation
Pattern analysis
Pattern recognition
Space technology
Vectors
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Summary:It has been proved a lot of linear feature extraction methods can be generalized to the nonlinear learning methods by using kernel methods. In this paper, a new nonlinear learning method of optimal transformation and cluster centers (OT-CC) is presented by using kernel technique. It is named as optimal transformation and cluster centers algorithm of kernel space (KOT-CC), which is a powerful technique for extracting nonlinear discriminant features and is very effective in solving pattern recognition problems where the overlap between patterns is serious. A large number of experiments demonstrate the new algorithm outperforms OT-CC and kernel fisher discriminant analysis (KFDA).
ISBN:1424426316
9781424426317
ISSN:2152-7431
2152-744X
DOI:10.1109/ICMA.2008.4798806