Discriminative Semi-Supervised Feature Selection via Rescaled Least Squares Regression-Supplement
DOI:
https://doi.org/10.1609/aaai.v32i1.12177Keywords:
Feature Selection;Semi-Supervised Feature Selection;Rescaled Linear Square RegressionAbstract
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). https://doi.org/10.1609/aaai.v32i1.12177
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Student Abstract Track