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Ellipsoidal support vector data description

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Abstract

This paper presents a data domain description formed by the minimum volume covering ellipsoid around a dataset, called “ellipsoidal support vector data description (eSVDD).” The method is analogous to support vector data description (SVDD), but instead, with an ellipsoidal domain description allowing tighter space around the data. In eSVDD, a hyperellipsoid extends its ability to describe more complex data patterns by kernel methods. This is explicitly achieved by defining an “empirical feature map” to project the images of given samples to a higher-dimensional space. We compare the performance of the kernelized ellipsoid in one-class classification with SVDD using standard datasets.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments to improve this manuscript.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand

    Kasemsit Teeyapan & Nipon Theera-Umpon

  2. Biomedical Engineering Center, Chiang Mai University, Chiang Mai, 50200, Thailand

    Nipon Theera-Umpon & Sansanee Auephanwiriyakul

  3. Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand

    Sansanee Auephanwiriyakul

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  1. Kasemsit Teeyapan
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Correspondence to Nipon Theera-Umpon.

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Teeyapan, K., Theera-Umpon, N. & Auephanwiriyakul, S. Ellipsoidal support vector data description. Neural Comput & Applic 28 (Suppl 1), 337–347 (2017). https://doi.org/10.1007/s00521-016-2343-3

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