Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition
This addresses robust signal recovery for applications like face recognition, but it appears incremental as it builds on existing Bayesian methods with a focus on handling time-varying outliers.
The paper tackles the problem of recovering block sparse signals in the presence of non-stationary outliers, and the result shows superiority over competing methods in synthetic data and face recognition applications.
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.