# L1-Regularized Functional Support Vector Machine

arXiv: https://arxiv.org/abs/2508.05567 · HTML: https://mlsanity.com/paper/2508.05567/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Hinge-Loss Markov Random Fields and Probabilistic Soft Logic](https://mlsanity.com/paper/1505.04406/) (2015)
- [Grouped variable importance with random forests and application to multiple functional data analysis](https://mlsanity.com/paper/1411.4170/) (2014)
- [Optimal Bayes Classifiers for Functional Data and Density Ratios](https://mlsanity.com/paper/1605.03707/) (2016)
- [A reproducing kernel Hilbert space framework for functional data classification](https://mlsanity.com/paper/2103.04504/) (2021)

## Abstract (arXiv)

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 L1-regularized functional support vector machine for binary classification. An accompanying algorithm is developed to fit the classifier. By imposing an L1 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.