Selecting training points for one-class support vector machines

Yuhua Li

    Research output: Contribution to journal › Article

    44 Citations (Scopus)

    Abstract

    This paper proposes a training points selection method for one-class support vector machines. It exploits the feature of a trained one-class SVM, which uses points only residing on the exterior region of data distribution as support vectors. Thus, the proposed training set reduction method selects the so-called extreme points which sit on the boundary of data distribution, through local geometry and k-nearest neighbours. Experimental results demonstrate that the proposed method can reduce training set considerably, while the obtained model maintains generalization capability to the level of a model trained on the full training set, but uses less support vectors and exhibits faster training speed.
    LanguageEnglish
    Pages1517-1522
    JournalPattern Recognition Letters
    Volume32
    Issue number11
    DOIs
    Publication statusPublished - 1 Aug 2011

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    Support vector machines
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    Li, Yuhua. / Selecting training points for one-class support vector machines. In: Pattern Recognition Letters. 2011 ; Vol. 32, No. 11. pp. 1517-1522.
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    Li, Y 2011, 'Selecting training points for one-class support vector machines', Pattern Recognition Letters, vol. 32, no. 11, pp. 1517-1522. https://doi.org/10.1016/j.patrec.2011.04.013

    Selecting training points for one-class support vector machines. / Li, Yuhua.

    In: Pattern Recognition Letters, Vol. 32, No. 11, 01.08.2011, p. 1517-1522.

    Research output: Contribution to journal › Article

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    AB - This paper proposes a training points selection method for one-class support vector machines. It exploits the feature of a trained one-class SVM, which uses points only residing on the exterior region of data distribution as support vectors. Thus, the proposed training set reduction method selects the so-called extreme points which sit on the boundary of data distribution, through local geometry and k-nearest neighbours. Experimental results demonstrate that the proposed method can reduce training set considerably, while the obtained model maintains generalization capability to the level of a model trained on the full training set, but uses less support vectors and exhibits faster training speed.

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    Li Y. Selecting training points for one-class support vector machines. Pattern Recognition Letters. 2011 Aug 1;32(11):1517-1522. https://doi.org/10.1016/j.patrec.2011.04.013

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