Sparse Signal Recovery in the Presence of Intra-Vector and Inter-Vector Correlation

2012/05/20 by Bhaskar D. Rao, Zhilin Zhang, Rao, Bhaskar D. +3
Computer Science · Engineering · Mathematics · #Blind Source Separation Techniques #FOS: Computer and information sciences #Image and Signal Denoising Methods #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Sparse and Compressive Sensing Techniques #cs.IT #cs.LG #math.IT #stat.ME #stat.ML

paper · pdf · doi:10.48550/arxiv.1205.4471

Invited review paper of 2012 International Conference on Signal Processing and Communications (SPCOM 2012)

arxiv created 2012/05/20 · openalex publication_date 2012/05/20 · arxiv updated 2012/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This work discusses the problem of sparse signal recovery when there is correlation among the values of non-zero entries. We examine intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector model, as well as their combination. Algorithms based on the sparse Bayesian learning are presented and the benefits of incorporating correlation at the algorithm level are discussed. The impact of correlation on the limits of support recovery is also discussed highlighting the different impact intra-vector and inter-vector correlations have on such limits.

Related

• Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity
• Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation
• Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning
• Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors
• Unlabelled Sensing: A Sparse Bayesian Learning Approach