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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Regularized Difference Criterion for Computing Discriminants for Dimensionality Reduction


    Contributors:


    Publication date :

    2017-10-01


    Size :

    2188768 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Difference Equations and Discriminants for Discrete Orthogonal Polynomials

    Ismail, M. E. / Nikolova, I. / Simeonov, P. | British Library Online Contents | 2005


    Object Recognition Using Boosted Discriminants

    Mahamud, S. / Hebert, M. / Shi, J. et al. | British Library Conference Proceedings | 2001


    Object recognition using boosted discriminants

    Mahamud, S. / Hebert, M. / Jianbo Shi, | IEEE | 2001


    Dimensionality reduction for probabilistic movement primitives

    Colomé Figueras, Adrià / Neumann, Gerhard / Peters, Jan et al. | BASE | 2014

    Free access