Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition

TL;DR

This paper introduces a robust Bayesian method for recovering block sparse signals that can handle non-stationary outliers, demonstrating superior performance in synthetic data and face recognition tasks.

Contribution

A novel Bayesian approach that effectively recovers block sparse signals with time-varying outliers, advancing robustness in signal processing applications.

Findings

Outperforms competing methods in synthetic experiments

Effective in face recognition with non-stationary outliers

Handles time-varying support of outliers successfully

Abstract

In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers. The key advantage of our proposed method is the ability to handle non-stationary outliers, i.e. outliers which have time varying support. We validate our approach with empirical results showing the superiority of the proposed method over competing approaches in synthetic data experiments as well as the multiple measurement face recognition problem.