Kernel fisher discriminant analysis (KFDA) has been widely used in fault diagnosis. In this paper, 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. Experimental results show the effectiveness of our method.
An improved Kernel method for fault diagnosis
01.12.2008
477924 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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