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

Authors

  • Guowen Yuan Shenzhen University
  • Xiaojun Chen Shenzhen University
  • Chen Wang Shenzhen University
  • Feiping Nie Northwestern Polytechnical University
  • Liping Jing Beijing Jiaotong University

Keywords:

Feature Selection;Semi-Supervised Feature Selection;Rescaled Linear Square Regression

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.

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Published

2018-04-29

How to Cite

Yuan, G., Chen, X., Wang, C., Nie, F., & Jing, L. (2018). Discriminative Semi-Supervised Feature Selection via Rescaled Least Squares Regression-Supplement. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12177