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Fast distant support vector data description

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Abstract

As an indispensable approach of one class classification, support vector data description (SVDD) has been studied within diverse research areas and application domains. Distant SVDD (dSVDD) is a variant of SVDD that shows higher identification accuracy. However, dSVDD is caught by the pricy cost and troublesome parameterization, which diminishes its popularity. This paper proposes a fast distant SVDD (fdSVDD) algorithm that addresses above two problems while maintaining the performance. To this end, a new objective that is equivalent to dSVDD’s original objective is proposed firstly; then the least square version of such a new objective serves as the objective of fdSVDD; finally, fdSVDD is implemented by solving a set of linear equations. To handle the parameterization problem, a data-derived heuristic is given. To foster the efficiency, fdSVDD is equipped with the reduction strategy of training data and the specification strategy of support vectors. And in the existence of negative data, fdSVDD is extended to fast parallel SVDD (fpSVDD). In experiments on real datasets, the proposed algorithms exhibit obvious improvement in efficiency and competitive behaviors compared with the peers.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China

    Ping Ling

  2. Department of Training, Air Force Logistics of P. L. A, Xuzhou, 221000, China

    Xiangyang You, Dajin Gao & Tao Gao

  3. School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, 4072, Australia

    Xue Li

Authors
  1. Ping Ling
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  2. Xiangyang You
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  3. Dajin Gao
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  4. Tao Gao
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  5. Xue Li
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Corresponding author

Correspondence to Ping Ling.

Additional information

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61105129, the key Natural Science Foundation of Jiangsu Normal University under Grant No. 15XLA07, ARC Discovery Project of Australia (2016–2018): Opinion Analysis on Objects in Social Networks.

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Ling, P., You, X., Gao, D. et al. Fast distant support vector data description. Memetic Comp. 9, 3–14 (2017). https://doi.org/10.1007/s12293-016-0189-y

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  • DOI: https://doi.org/10.1007/s12293-016-0189-y

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