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Semi-supervised Uncorrelated Feature Selection

EasyChair Preprint no. 1551

2 pagesDate: September 23, 2019


In this paper, we propose an uncorrelated feature selection method for semi-supervised feature selection task, namely SSUFS. The new method extends the Rescaled Linear Square Regression by imposing an Uncorrelated Regularization (UR) to select only a small number of important features from highly correlated features. With this regularization, the new method is able to select lowly-nonlinear-correlated important features. SSUFS was compared with 5 feature selection methods on 5 datasets and the experimental results show the superior performance of the new method.

Keyphrases: feature selection, Nonlinear Uncorrelated Feature Selection, semi-supervised learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Weichan Zhong and Xiaojun Chen and Feiping Nie},
  title = {Semi-supervised Uncorrelated Feature Selection},
  howpublished = {EasyChair Preprint no. 1551},

  year = {EasyChair, 2019}}
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