An overview on nonparallel hyperplane support vector machine algorithms

Shifei Ding, Xiaopeng Hua, Junzhao Yu

Abstract: Support vector machine (SVM) has attracted substantial interest in the community of machine learning. As the extension of SVM, nonparallel hyperplane SVM (NHSVM) classification algorithms have become current researching hot spots in machine learning during the last few years. For binary classification tasks, the idea of NHSVM algorithms is to find a hyperplane for each class, such that each hyperplane is proximal to the data points of one class and far from the data points of the other class. Compared with the classical SVM, NHSVM algorithms have lower computational complexity, work better on XOR problems and can get better generalization performance. This paper reviews three representative NHSVM algorithms, including generalized eigenvalue proximal SVM (GEPSVM), twin SVM (TWSVM) and projection twin SVM (PTSVM), and gives the research progress of them. The aim of this overview is to provide an insightful organization of current developments of NHSVM algorithms, identify their limitations and give suggestions for further research.