Kernel parameter optimization for kernel-based LDA methods

Jian Huang*, Xiaoming Chen, Pong Chi YUEN, Jun Zhang, W. S. Chen, J. H. Lai

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)


Kernel approach has been employed to solve classification problem with complex distribution by mapping the input space to higher dimensional feature space. However, one of the crucial factors in the Kernel approach is the choosing of kernel parameters which highly affect the performance and stability of the kernel-based learning methods. In view of this limitation, this paper adopts the Eigenvalue Stability Bounded Margin Maximization (ESBMM) algorithm to automatically tune the multiple kernel parameters for Kernel-based LDA methods. To demonstrate its effectiveness, the ESBMM algorithm has been extended and applied on two existing kernelbased LDA methods. Experimental results show that after applying the ESBMM algorithm, the performance of these two methods are both improved.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Number of pages7
Publication statusPublished - 2008
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: 1 Jun 20088 Jun 2008

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2008 International Joint Conference on Neural Networks, IJCNN 2008
CityHong Kong

Scopus Subject Areas

  • Software
  • Artificial Intelligence

User-Defined Keywords

  • Face recognition
  • Kernel fisher discriminant
  • Kernel parameter
  • Stability

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