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A sparsity-based training algorithm for least squares SVM

Conference Paper


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Abstract


  • We address the training problem of the sparse

    Least Squares Support Vector Machines (SVM) using compressed

    sensing. The proposed algorithm regards the support

    vectors as a dictionary and selects the important ones that

    minimize the residual output error iteratively. A measurement

    matrix is also introduced to reduce the computational

    cost. The main advantage is that the proposed algorithm

    performs model training and support vector selection simultaneously.

    The performance of the proposed algorithm

    is tested with several benchmark classification problems in

    terms of number of selected support vectors and size of

    the measurement matrix. Simulation results show that the

    proposed algorithm performs competitively when compared

    to existing methods.

Publication Date


  • 2014

Citation


  • Yang, J. & Ma, J. (2014). A sparsity-based training algorithm for least squares SVM. IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014) (pp. 345-350). United States: Institute of Electrical and Electronics Engineers.

Scopus Eid


  • 2-s2.0-84925158214

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5060&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4041

Start Page


  • 345

End Page


  • 350

Abstract


  • We address the training problem of the sparse

    Least Squares Support Vector Machines (SVM) using compressed

    sensing. The proposed algorithm regards the support

    vectors as a dictionary and selects the important ones that

    minimize the residual output error iteratively. A measurement

    matrix is also introduced to reduce the computational

    cost. The main advantage is that the proposed algorithm

    performs model training and support vector selection simultaneously.

    The performance of the proposed algorithm

    is tested with several benchmark classification problems in

    terms of number of selected support vectors and size of

    the measurement matrix. Simulation results show that the

    proposed algorithm performs competitively when compared

    to existing methods.

Publication Date


  • 2014

Citation


  • Yang, J. & Ma, J. (2014). A sparsity-based training algorithm for least squares SVM. IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014) (pp. 345-350). United States: Institute of Electrical and Electronics Engineers.

Scopus Eid


  • 2-s2.0-84925158214

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5060&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4041

Start Page


  • 345

End Page


  • 350