Semi-supervised Feature Selection via Rescaled Linear Regression

  title={Semi-supervised Feature Selection via Rescaled Linear Regression},
  author={Xiaojun Chen and Guowen Yuan and Feiping Nie and Joshua Zhexue Huang},
With the rapid increase of complex and highdimensional sparse data, demands for new methods to select features by exploiting both labeled and unlabeled data have increased. Least regression based feature selection methods usually learn a projection matrix and evaluate the importances of features using the projection matrix, which is lack of theoretical explanation. Moreover, these methods cannot find both global and sparse solution of the projection matrix. In this paper, we propose a novel… CONTINUE READING

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