L1-Regularized Functional Support Vector Machine
This work addresses the gap in functional data analysis for multivariate functional covariates, offering a method for binary classification and feature selection, though it appears incremental as it extends existing techniques.
The paper tackled binary classification with multivariate functional covariates by proposing an L1-regularized functional support vector machine, demonstrating good performance in prediction and feature selection through simulations and a real-world application.
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.