Mean field variational Bayesian inference for support vector machine classification

TL;DR

This paper introduces a mean field variational Bayesian method for SVM classification that automatically handles penalty tuning, dependent data, missing values, and variable selection, demonstrating superior performance and flexibility.

Contribution

It presents a novel Bayesian variational approach to SVMs that overcomes classical limitations and adapts to complex data scenarios.

Findings

Outperforms classical SVMs on simulated and real datasets

Automatically selects penalty parameters

Handles dependent samples and missing data

Abstract

A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of many of the shortcomings associated with classical SVMs including automatic penalty parameter selection, the ability to handle dependent samples, missing data and variable selection. We demonstrate on simulated and real datasets that our approach is easily extendable to non-standard situations and outperforms the classical SVM approach whilst remaining computationally efficient.