One-sided best fitting hyperplane classifier

In this paper we propose a new hyperplane fitting classification method that does not have limitations of the existing hyperplane fitting classifiers. There are two principal improvements of the proposed method: It returns sparse solutions and it is suitable for large-scale problems. Both advantages are accomplished by using a simple trick, which constraints positive samples to lie between two parallel hyperplanes rather than to lie on a single fitting hyperplane. The experiments on several databases show that our proposed method typically outperforms other hyperplane fitting classifiers in terms of classification accuracy, and it produces comparable results to the Support Vector Machine classifier.