Semisupervised Feature Selection With Sparse Discriminative Least Squares Regression

In big data time, selecting informative features has become an urgent need. However, due to the huge cost of obtaining enough labeled data for supervised tasks, researchers have turned their attention to semisupervised learning, which exploits both labeled and unlabeled data. In this article, we propose a sparse discriminative semisupervised feature selection (SDSSFS) method. In this method, the
$\epsilon $
-dragging technique for the supervised task is extended to the semisupervised task, which is used to enlarge the distance between classes in order to obtain a discriminative solution. The flexible
$\ell _{2,p}$
norm is implicitly used as regularization in the new model. Therefore, we can obtain a more sparse solution by setting smaller
$p$
. An iterative method is proposed to simultaneously learn the regression coefficients and
$\epsilon $
-dragging matrix and predicting the unknown class labels. Experimental results on ten real-world datasets show the superiority of our proposed method.