期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 2, 页码 1423-1432出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2007.11.043
关键词
Fault diagnosis; Kernel fisher discriminant analysis (KFDA); Feature vector selection (FVS); Nearest Feature line (NFL)
类别
资金
- National Natural Science Foundation of China [60704010]
This paper improves kernel fisher discriminant analysis (KFDA) for fault diagnosis from three aspects. Firstly, a feature vector selection (FVS) scheme based on a geometrical consideration is given to reduce the computational complexity of KFDA when the number of samples becomes large. Secondly, it ne v kernel function, called the Cosine kernel, is proposed to increase the discriminating capability of the original polynomial kernel function. Thirdly, nearest feature line (NFL) classifier is employed to further enhance the fault diagnosis performance when the sample number is very small. Experimental results show the effectiveness of our methods. (c) 2007 Elsevier Ltd. All rights reserved.
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