Efficient Top-K Feature Selection Using Coordinate Descent Method

Sparse learning based feature selection has been widely investigated in recent years. In this study, we focus on the l(2,0)-norm based feature selection, which is effective for exact top-k feature selection but challenging to optimize. To solve the general l(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 l(2,0)-norm constraint can be solved efficiently by solving the selection matrix with CD method. We impose the l(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 l(2,0)-norm to identify informative features. More importantly, the versatility of CD-LSR facilitates the applications of the l(2,0)-norm in more sophisticated models. Based on the competitive performance of l(2,0)-norm on the baseline LSR model, the satisfactory performance of its applications is reasonably expected. The source MATLAB code are available at: https://github.com/solerxl/Code For AAAI 2023.