Covariance-guided One-Class Support Vector Machine

作者：

Highlights：

• The low-variance directions are crucial for one-class classification (OCC).

• A new method of OCC emphasizing the low-variance directions is proposed.

• The method incorporates covariance information into convex optimization problem.

• Can be implemented and solved efficiently with existing software.

• Comparative experiments with contemporary classifiers show positive results.

摘要

Highlights•The low-variance directions are crucial for one-class classification (OCC).•A new method of OCC emphasizing the low-variance directions is proposed.•The method incorporates covariance information into convex optimization problem.•Can be implemented and solved efficiently with existing software.•Comparative experiments with contemporary classifiers show positive results.

论文关键词：Covariance,Support Vector Machine,One-class classification,Outlier detection

论文评审过程：Received 26 March 2013, Revised 1 October 2013, Accepted 1 January 2014, Available online 15 January 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.01.004