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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    Titel :

    An improved Kernel method for fault diagnosis


    Beteiligte:
    Cui, J. F. (Autor:in) / Guo, G. S. (Autor:in) / Miao, M. X. (Autor:in) / Liu, S. X. (Autor:in)


    Erscheinungsdatum :

    01.12.2008


    Format / Umfang :

    477924 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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