2017/11/06 by Hang Xiao, Xiao, Hang, Zhengli Xing +7
Computer Science · Engineering · Mathematics · #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #cs.IT #cs.LG #eess.SP #electronic engineering #information engineering #math.IT #stat.ML
paper · pdf · doi:10.48550/arxiv.1711.01790
arxiv created 2017/11/06 · openalex publication_date 2017/11/06 · arxiv updated 2017/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method for recovery of common row sparsity MMV signals, where a pattern-coupled hierarchical Gaussian prior model is introduced to characterize both the block-sparsity of the coefficients and the statistical dependency between neighboring coefficients of the common row sparsity MMV signals. Unlike many other methods, the proposed method is able to automatically capture the block sparse structure of the unknown signal. Our method is developed using an expectation-maximization (EM) framework. Simulation results show that our proposed method offers competitive performance in recovering block-sparse common row sparsity pattern MMV signals.