Dca for sparse quadratic kernel-free least squares semi-supervised support vector machine

HIGHLIGHTS

• who: Jun Sun and Wentao Qu from the Department of Applied Mathematics, Beijing Jiaotong University, Beijing, China have published the research: DCA for Sparse Quadratic Kernel-Free Least Squares Semi-Supervised Support Vector Machine, in the Journal: Mathematics 2022, 10, 2714. of /2022/
• what: The authors propose a strong sparse quadratic kernel-free least vector machine (SSQLSS3 V M) in which the authors add a ℓ0 norm regularization term to make it sparse. The authors provide the framework of sGS-ADMM for solving and its more details are shown in Algorithm 2 below.
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