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| Feature selection plays a significant role in pattern recognition, machine learning and other related fields. It can be used in data preprocessing step to achieve efficient dimensionality reduction by selecting a subset of discriminant and informative features. In recent years, regularization based feature selection has attracted great interest and many algorithms have been proposed. The common idea of these methods is to choose a proper norm such as $ell_1$-norm, $ell_1/ell_infty$-norm and $ell_{2,1}$-norm for the selection vector or selection matrix to achieve sparseness, and the non-zero entries or rows correspond to the selected features. This paper aims to review the recently proposed representative works, provides insight into the differences and connections between these methods, and discusses the challenges and possible directions of future work. |
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| Keywords:pattern recognition; feature selection; regularization |
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