Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
Kernel parameter optimization for kernel-based LDA methods
- Authors
- Huang, Jian; Chen, Xiaoming; Yuen, P.C.; Zhang, Jun; Chen, W.S.; Lai, J.H.
- Issue Date
- Sep-2008
- Publisher
- IEEE
- Keywords
- Face recognition; Kernel fisher discriminant; Kernel parameter; Stability
- Citation
- 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp 3840 - 3846
- Pages
- 7
- Indexed
- SCI
SCOPUS
- Journal Title
- 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
- Start Page
- 3840
- End Page
- 3846
- ISSN
- 2161-4393
- Abstract
- 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. © 2008 IEEE.
- Files in This Item
- Go to Link
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Related Researcher
- ZHANG, Jun
- ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)