Regularization approaches for support vector machines with applications to biomedical data

TL;DR

This paper explores various regularization techniques for support vector machines, emphasizing their advantages in biomedical data analysis where interpretability and sparsity are crucial.

Contribution

It introduces and compares different regularization methods for SVMs, highlighting their effectiveness in biomedical applications.

Findings

Regularization approaches improve interpretability of SVMs in biomedical data

Sparsity-inducing regularizations enhance feature selection

Different regularizations perform variably depending on dataset characteristics

Abstract

The support vector machine (SVM) is a widely used machine learning tool for classification based on statistical learning theory. Given a set of training data, the SVM finds a hyperplane that separates two different classes of data points by the largest distance. While the standard form of SVM uses L2-norm regularization, other regularization approaches are particularly attractive for biomedical datasets where, for example, sparsity and interpretability of the classifier's coefficient values are highly desired features. Therefore, in this paper we consider different types of regularization approaches for SVMs, and explore them in both synthetic and real biomedical datasets.