The trace kernel bandwidth criterion for support vector data description
作者：
Highlights：
• Support Vector Data Description (SVDD) is a popular kernel-based unsupervised one-class classification method. The Gaussian kernel is the most common used kernel.
• The Gaussian kernel has a tuning parameter, the kernel bandwidth, and it is important to choose it correctly.
• We propose an automated, unsupervised, bandwidth selection method for SVDD.
• Our proposed bandwidth is also appropriate for selecting the bandwidth for One Class Support Vector Machines (OCSVM).
摘要
•Support Vector Data Description (SVDD) is a popular kernel-based unsupervised one-class classification method. The Gaussian kernel is the most common used kernel.•The Gaussian kernel has a tuning parameter, the kernel bandwidth, and it is important to choose it correctly.•We propose an automated, unsupervised, bandwidth selection method for SVDD.•Our proposed bandwidth is also appropriate for selecting the bandwidth for One Class Support Vector Machines (OCSVM).
论文关键词：Support vector data description,SVDD,One-class support vector machines,OCSVM,Gaussian kernel,Automatic tuning,Gaussian kernel bandwidth
论文评审过程：Received 10 February 2020, Revised 31 August 2020, Accepted 18 September 2020, Available online 24 September 2020, Version of Record 4 October 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107662
