Adaptive L0 Regularization for Sparse Support Vector Regression

In this work, we proposed a sparse version of the Support Vector Regression (SVR) algorithm that uses regularization to achieve sparsity in function estimation. To achieve this, we used an adaptive L-0 penalty that has a ridge structure and, therefore, does not introduce additional computational complexity to the algorithm. In addition to this, we used an alternative approach based on a similar proposal in the Support Vector Machine (SVM) literature. Through numerical studies, we demonstrated the effectiveness of our proposals. We believe that this is the first time someone discussed a sparse version of Support Vector Regression (in terms of variable selection and not in terms of support vector selection).

Automated summary

In this study, we propose a sparse version of Support Vector Regression (SVR) that achieves sparsity in function estimation through regularization. We introduce an adaptive L-0 penalty with a ridge structure, which does not increase computational complexity. Additionally, we adopt an alternative approach based on a similar proposal in the Support Vector Machine (SVM) literature. Numerical studies confirm the effectiveness of our novel approach, which focuses on variable selection rather than support vector selection.