A Novel Dispersion Covariance-guided One-Class Support Vector Machines

Soumaya Nheri, Riadh Ksantini, Mohamed-Bécha Kaâniche, Adel Bouhoula

Abstract

In order to handle spherically distributed data, in a proper manner, we intend to exploit the subclass information. In one class classification process, many recently proposed methods try to incorporate subclass information in the standard optimization problem. We presume that we should minimize the within-class variance, instead of minimizing the global variance, with respect to subclass information. Covariance-guided One-Class Support Vector Machine (COSVM) emphasizes the low variance direction of the training dataset which results in higher accuracy. However, COSVM does not handle multi-modal target class data. More precisely, it does not take advantage of target class subclass information. Therefore, to reduce the dispersion of the target data with respect to newly obtained subclass information, we express the within class dispersion and we incorporate it in the optimization problem of the COSVM. So, we introduce a novel variant of the COSVM classifier, namely Dispersion COSVM, that exploits subclass information in the kernel space, in order to jointly minimize the dispersion within and between subclasses and improve classification performance. A comparison of our method to contemporary one-class classifiers on numerous real data sets demonstrate clearly its superiority in terms of classification performance.

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Paper Citation


in Harvard Style

Nheri S., Ksantini R., Kaâniche M. and Bouhoula A. (2020). A Novel Dispersion Covariance-guided One-Class Support Vector Machines.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-402-2, pages 546-553. DOI: 10.5220/0009174205460553


in Bibtex Style

@conference{visapp20,
author={Soumaya Nheri and Riadh Ksantini and Mohamed-Bécha Kaâniche and Adel Bouhoula},
title={A Novel Dispersion Covariance-guided One-Class Support Vector Machines},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2020},
pages={546-553},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009174205460553},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - A Novel Dispersion Covariance-guided One-Class Support Vector Machines
SN - 978-989-758-402-2
AU - Nheri S.
AU - Ksantini R.
AU - Kaâniche M.
AU - Bouhoula A.
PY - 2020
SP - 546
EP - 553
DO - 10.5220/0009174205460553