Regularization approaches for support vector machines with applications to biomedical data
This work addresses the need for more interpretable and sparse classifiers in biomedical applications, but it appears incremental as it builds on existing SVM methods.
The paper tackles the problem of improving support vector machines (SVMs) for biomedical data by exploring different regularization approaches beyond standard L2-norm, focusing on sparsity and interpretability, and tests them on synthetic and real datasets.
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.