Adaptive L0 Regularization for Sparse Support Vector Regression

Christou, Antonis and Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 2023. Adaptive L0 Regularization for Sparse Support Vector Regression. Mathematics 11 (13) , 2808. 10.3390/math11132808

PDF - Accepted Post-Print Version Available under License Creative Commons Attribution. Download (291kB)

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 L0 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).

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Mathematics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	MDPI
Date of First Compliant Deposit:	10 July 2023
Date of Acceptance:	20 June 2023
Last Modified:	10 Jul 2023 09:59
URI:	https://orca.cardiff.ac.uk/id/eprint/160887

Actions (repository staff only)

Edit Item