Sparse-Group Bayesian Feature Selection Using Expectation Propagation for Signal Recovery and Network Reconstruction

TL;DR

This paper introduces a fast Bayesian feature selection method using expectation propagation that effectively leverages grouping information, outperforming traditional methods in signal recovery and gene network reconstruction.

Contribution

The paper presents a novel Bayesian sparse-group feature selection approach with expectation propagation, improving accuracy and computational efficiency over existing methods.

Findings

Accurately recovers parameters in signal recovery tasks.

Effectively reconstructs gene regulatory networks.

Operates efficiently on large-scale problems.

Abstract

We present a Bayesian method for feature selection in the presence of grouping information with sparsity on the between- and within group level. Instead of using a stochastic algorithm for parameter inference, we employ expectation propagation, which is a deterministic and fast algorithm. Available methods for feature selection in the presence of grouping information have a number of short-comings: on one hand, lasso methods, while being fast, underestimate the regression coefficients and do not make good use of the grouping information, and on the other hand, Bayesian approaches, while accurate in parameter estimation, often rely on the stochastic and slow Gibbs sampling procedure to recover the parameters, rendering them infeasible e.g. for gene network reconstruction. Our approach of a Bayesian sparse-group framework with expectation propagation enables us to not only recover accurate parameter estimates in signal recovery problems, but also makes it possible to apply this Bayesian framework to large-scale network reconstruction problems. The presented method is generic but in terms of application we focus on gene regulatory networks. We show on simulated and experimental data that the method constitutes a good choice for network reconstruction regarding the number of correctly selected features, prediction on new data and reasonable computing time.