Download PDFOpen PDF in browserSemi-supervised Uncorrelated Feature SelectionEasyChair Preprint 15512 pages•Date: September 23, 2019AbstractIn 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: Nonlinear Uncorrelated Feature Selection, feature selection, semi-supervised learning
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