A kernel-free quadratic surface support vector machine for semi-supervised learning

Abstract

In this paper, we propose a kernel-free semi-supervised quadratic surface support vector machine model for binary classification. The model is formulated as a mixed-integer programming problem, which is equivalent to a non-convex optimization problem with absolute-value constraints. Using the relaxation techniques, we derive a semi-definite programming problem for semi-supervised learning. By solving this problem, the proposed model is tested on some artificial and public benchmark data sets. Preliminary computational results indicate that the proposed method outperforms some existing well-known methods for solving semi-supervised support vector machine with a Gaussian kernel in terms of classification accuracy.

Keywords:

• semi-supervised support vector machine
• quadratic surface support vector machine
• semi-supervised learning
• kernel-free
• semi-definite relaxation

Acknowledgements

This research was partially supported by the National Natural Science Foundation of China Grant (No.11371242) and US National Science Foundation Grant (No. DMI-0553310).