Non-parallel hyperplanes ordinal regression machine

Abstract

This paper proposes a method to solve ordinal regression problems, namely the non-parallel hyperplanes ordinal regression machine (NPHORM). The goal of this approach is to find K different hyperplanes for the K classes with ordinal information, so that each class is as close as possible to the corresponding hyperplane while as far as possible from the adjacent to the left and right classes. The more flexible separate hyperplanes are preferred using the order information of the data. As a result, this approach only needs to solve K quadratic programming problems independently. Our approach NPHORM is validated on 2 artificial datasets, 16 discretized regression datasets and 17 real ordinal regression datasets and compared with 8 outstanding SVM-based ordinal regression approaches. The results show that our approach NPHORM is comparable with the other SVM-based approaches, especially in real ordinal regression datasets. In addition, our NPHORM is also carried out on the historical color image dataset to compare the performance of deep learning method. Experimental results demonstrate that the performance of our NPHORM outperforms the deep learning methods on MAE.