Covariance-guided One-Class Support Vector Machine
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.
covariance support vector machine one-class classification outlier detection