$L_1$-regularized functional support vector machine

In functional data analysis, binary classification with one functional covariate has been extensively studied. We aim to fill in the gap of considering multivariate functional covariates in classification. In particular, we propose an $L_1$-regularized functional support vector machine for binary classification. An accompanying algorithm is developed to fit the classifier. By imposing an $L_1$ penalty, the algorithm enables us to identify relevant functional covariates of the binary response. Numerical results from simulations and one real-world application demonstrate that the proposed classifier enjoys good performance in both prediction and feature selection.

Keywords

functional data classification, feature selection, B splines, gradient descent

The full text of this article is unavailable through your IP address: 172.17.0.1

Received 13 August 2022

Accepted 26 December 2022

Published 19 July 2024