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

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

AAAI 2018 pp. 8177-8178

doi:10.1609/AAAI.V32I1.12177 /aaai/2018/yuan2018aaai-discriminative/

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.

PDF AAAI Semantic Scholar

Cite

Text

Yuan et al. "Discriminative Semi-Supervised Feature Selection via Rescaled Least Squares Regression-Supplement." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12177

Markdown

[Yuan et al. "Discriminative Semi-Supervised Feature Selection via Rescaled Least Squares Regression-Supplement." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/yuan2018aaai-discriminative/) doi:10.1609/AAAI.V32I1.12177

BibTeX

@inproceedings{yuan2018aaai-discriminative,
  title     = {{Discriminative Semi-Supervised Feature Selection via Rescaled Least Squares Regression-Supplement}},
  author    = {Yuan, Guowen and Chen, Xiaojun and Wang, Chen and Nie, Feiping and Jing, Liping},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8177-8178},
  doi       = {10.1609/AAAI.V32I1.12177},
  url       = {https://mlanthology.org/aaai/2018/yuan2018aaai-discriminative/}
}