An Efficient ADMM-Based Algorithm to Nonconvex Penalized Support Vector Machines
This work addresses a computational bottleneck for researchers and practitioners using SVMs with sparsity-inducing penalties, though it is incremental as it builds on existing ADMM methods.
The paper tackles the challenge of solving nonconvex penalized support vector machines (SVMs) by proposing an efficient ADMM-based algorithm, which demonstrates superior performance on five benchmark datasets compared to three state-of-the-art approaches.
Support vector machines (SVMs) with sparsity-inducing nonconvex penalties have received considerable attentions for the characteristics of automatic classification and variable selection. However, it is quite challenging to solve the nonconvex penalized SVMs due to their nondifferentiability, nonsmoothness and nonconvexity. In this paper, we propose an efficient ADMM-based algorithm to the nonconvex penalized SVMs. The proposed algorithm covers a large class of commonly used nonconvex regularization terms including the smooth clipped absolute deviation (SCAD) penalty, minimax concave penalty (MCP), log-sum penalty (LSP) and capped-$\ell_1$ penalty. The computational complexity analysis shows that the proposed algorithm enjoys low computational cost. Moreover, the convergence of the proposed algorithm is guaranteed. Extensive experimental evaluations on five benchmark datasets demonstrate the superior performance of the proposed algorithm to other three state-of-the-art approaches.