Discriminative Semi-Supervised Feature Selection via Rescaled Least Squares Regression-Supplement

Guowen Yuan, Xiaojun Chen, Chen Wang, Feiping Nie, Liping Jing

Abstract: In this paper, we propose a Discriminative Semi-Supervised Feature Selection (DSSFS) method. In this method, a ε-dragging technique is introduced to the Rescaled Linear Square Regression in order to enlarge the distances between different classes. An iterative method is proposed to simultaneously learn the regression coefficients, ε-draggings matrix and predicting the unknown class labels. Experimental results show the superiority of DSSFS.