Hyperspectral data classification has shown potential in many applications. However, a large number of spectral bands cause overfitting. Methods for reducing spectral bands, e.g., linear discriminant analysis, require matrix inversion. We propose a semidefinite programming for linear discriminants regularized difference (SLRD) criterion approach that does not require matrix inversion. The paper establishes a classification error bound and provides experimental results with ten methods over six hyperspectral datasets demonstrating the efficacy of the proposed SLRD technique.


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    Titel :

    Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction


    Beteiligte:
    Aved, Alex J. (Autor:in) / Blasch, Erik P. (Autor:in) / Jing Peng, (Autor:in)


    Erscheinungsdatum :

    2017-10-01


    Format / Umfang :

    2188768 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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