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.


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    Title :

    An improved Kernel method for fault diagnosis


    Contributors:
    Cui, J. F. (author) / Guo, G. S. (author) / Miao, M. X. (author) / Liu, S. X. (author)


    Publication date :

    2008-12-01


    Size :

    477924 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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