A novel incremental one-class support vector machine based on low variance direction

作者：

Highlights：

• An incremental One-Class Covariance-guided Support Vector Machine is proposed.

• The proposed classifier puts more emphasis on the low variance directions while keeping the basic formulation of One-Class Support Vector Machine untouched.

• The incremental procedure is introduced by controlling the possible changes of support vectors after the addition of new data points, thanks to the Karush–Kuhn–Tucker conditions.

• Comparative tests with contemporary incremental and batch one-class classifiers were performed to show the superiority of our method.

摘要

•An incremental One-Class Covariance-guided Support Vector Machine is proposed.•The proposed classifier puts more emphasis on the low variance directions while keeping the basic formulation of One-Class Support Vector Machine untouched.•The incremental procedure is introduced by controlling the possible changes of support vectors after the addition of new data points, thanks to the Karush–Kuhn–Tucker conditions.•Comparative tests with contemporary incremental and batch one-class classifiers were performed to show the superiority of our method.

论文关键词：One-Class classification,Incremental learning,Support vector machine,Low variance directions

论文评审过程：Received 17 November 2017, Revised 13 February 2019, Accepted 27 February 2019, Available online 27 February 2019, Version of Record 7 March 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.02.027