Adaptive L0 Regularization for Sparse Support Vector Regression

Antonis Christou and Andreas Artemiou ()
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Antonis Christou: School of Mathematics, Cardiff University, Cardiff CF24 4AG, UK
Andreas Artemiou: School of Mathematics, Cardiff University, Cardiff CF24 4AG, UK

Mathematics, 2023, vol. 11, issue 13, 1-12

Abstract: 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).

Keywords: variable selection; regularization; sparsity; support vector regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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