MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multicategory Classification
Lingfeng Wang, Xu-Yao Zhang, Chunhong Pan · IEEE Transactions on Neural Networks and Learning Systems · 2015
In this brief, we propose a new margin scalable discriminative least squares regression (MSDLSR) model for multicategory classification. The main motivation behind the MSDLSR is to explicitly control the margin of DLSR model. We first prove that the DLSR is a relaxation of the traditional L2-support vector machine. Based on this fact, we further provide a theorem on the margin of DLSR. With this theorem, we add an explicit constraint on DLSR to restrict the number of zeros of dragging values, so as to control the margin of DLSR. The new model is called MSDLSR. Theoretically, we analyze the determination of the margin and support vectors of MSDLSR. Extensive experiments illustrate that our method outperforms the current state-of-the-art approaches on various machine leaning and real-world data sets.