Open document
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General information |
| Author |
Liu, Xiao-Zhang; Feng, Guo-Can |
| Published |
InTech Open Access Publisher, 2013
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| Abstract |
Recent applications and developments based
on support vector machines (SVMs) have shown that
using multiple kernels instead of a single one can
enhance classifier performance. However, there are few
reports on performance of the kernel‐based Fisher
discriminant analysis (kernel‐based FDA) method with
multiple kernels. This paper proposes a multiple kernel
construction method for kernel‐based FDA. The
constructed kernel is a linear combination of several
base kernels with a constraint on their weights. By
maximizing the margin maximization criterion (MMC),
we present an iterative scheme for weight optimization.
The experiments on the FERET and CMU PIE face
databases show that, our multiple kernel Fisher
discriminant analysis (MKFD) achieves high recognition
performance, compared with single‐kernel‐based FDA.
The experiments also show that the constructed kernel
relaxes parameter selection for kernel‐based FDA to
some extent. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Published: 2004
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