Weighted Linear Loss Large Margin Distribution Machine for Pattern Classification

Abstract

Compared with support vector machine, large margin distribution machine (LDM) has better generalization performance. The central idea of LDM is to maximize the margin mean and minimize the margin variance simultaneously. But the computational complexity of LDM is high. In order to reduce the computational complexity of LDM, a weighted linear loss LDM (WLLDM) is proposed. The framework of WLLDM is built based on LDM and the weighted linear loss. The weighted linear loss is adopted instead of the hinge loss in WLLDM. This modification can transform the quadratic programming problem into a simple linear equation, resulting in lower computational complexity. Thus, WLLDM has the potential to deal with large-scale datasets. The WLLDM is similar in principle to the LDM algorithm, which can optimize the margin distribution and achieve better generalization performance. The WLLDM algorithm is compared with other models by conducting experiments on different datasets. The experimental results show that the proposed WLLDM has better generalization performance and faster training speed.