Ramp Loss based robust one-class SVM

- A new method is proposed to reduce outliers' influence on OCSVM model.
- Ramp Loss function is introduced into OCSVM optimization.
- An iterative algorithm is proposed to solve this new OCSVM optimization.
- The outliers are first identified and then removed from the training set.

One-class SVM (OCSVM) is widely adopted in one-class classification (OCC) fields. However, outliers in the training set negatively influence the classification surface of OCSVM, degrading its performance. To solve this problem, a novel method is proposed in this paper. This proposed method introduces Ramp Loss function into OCSVM optimization, so as to reduce outliers' influence. Then the outliers are identified and removed from the training set. The final classification surface is obtained on the remaining training samples. Various experiments verify the effectiveness of this proposed method.