Sparse Bayesian learning with automatic-weighting Laplace priors for sparse signal recovery

DOI10.1007/s00180-023-01354-4OpenAlexW4377099120MaRDI QIDQ6148403

Jin-Wei Sun, Zonglong Bai

Publication date: 7 February 2024

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-023-01354-4

zbMATH Keywords

sparse signal recoveryspace alternative variational estimationsparse Bayesian leaning

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• The solution path of the generalized lasso
• Conditioning of Random Block Subdictionaries With Applications to Block-Sparse Recovery and Regression
• 10.1162/15324430152748236
• An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem
• Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization
• Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
• Efficient Variational Bayesian Approximation Method Based on Subspace Optimization
• Super-Resolution Compressed Sensing for Line Spectral Estimation: An Iterative Reweighted Approach
• A GAMP-Based Low Complexity Sparse Bayesian Learning Algorithm
• Subspace Pursuit for Compressive Sensing Signal Reconstruction
• Sparse Bayesian DOA Estimation Using Hierarchical Synthesis Lasso Priors for Off-Grid Signals
• Model-Based Compressive Sensing
• Sparse solutions to linear inverse problems with multiple measurement vectors
• A sparse signal reconstruction perspective for source localization with sensor arrays
• Bayesian Compressive Sensing Using Laplace Priors
• Compressed sensing