Sparse learning based feature selection has been widely investigated in recent years. In this study, we focus on the $ \ell_{2,0} $-norm based feature selection, which is effective for exact top-$ k $ feature selection but challenging to optimize. To solve the general $ \ell_{2,0} $-norm constrained problems, we novelly develop a parameter-free optimization framework based on the coordinate descend (CD) method, termed CD-LSR. Specifically, we devise a skillful conversion from the original problem to solving one continuous matrix and one discrete selection matrix. Then the nontrivial $ \ell_{2,0} $-norm constraint can be solved efficiently by solving the selection matrix with CD method. We impose the $ \ell_{2,0} $-norm on a vanilla least square regression (LSR) model for feature selection and optimize it with CD-LSR. Extensive experiments exhibit the efficiency of CD-LSR, as well as the discrimination ability of $ \ell_{2,0} $-norm to identify informative features. More importantly, the versatility of CD-LSR facilitates the applications of the $ \ell_{2,0} $-norm in more sophisticated models. Based on the competitive performance of $ \ell_{2,0} $-norm on the baseline LSR model, the satisfactory performance of its applications is reasonably expected.

Efficient Top-k Feature Selection Using Coordinate Descent Method

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Lei Xu

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Efficient Top-k Feature Selection Using Coordinate Descent Method

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