2016/05/06 by I. A. Fedorov, Ritwik Giri, Fedorov, Igor +5
Computer Science · Engineering · #Blind Source Separation Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1605.02057
openalex publication_date 2016/05/06 · openalex created_date 2022/10/07 · openalex updated_date 2026/07/28
In this paper, we present a novel Bayesian approach to recover simultaneously\nblock sparse signals in the presence of outliers. The key advantage of our\nproposed method is the ability to handle non-stationary outliers, i.e. outliers\nwhich have time varying support. We validate our approach with empirical\nresults showing the superiority of the proposed method over competing\napproaches in synthetic data experiments as well as the multiple measurement\nface recognition problem.\n