Typical reconstruction performance for distributed compressed sensing based on ℓ2,1-norm regularized least square and Bayesian optimal reconstruction: influences of noise
DOI10.1088/1742-5468/2016/06/063304zbMath1456.94019OpenAlexW2440283021MaRDI QIDQ3302730
Yoshiyuki Kabashima, Yoshifumi Shiraki
Publication date: 11 August 2020
Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1088/1742-5468/2016/06/063304
Estimation in multivariate analysis (62H12) Analysis of algorithms (68W40) Bayesian inference (62F15) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Uses Software
Cites Work
- Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms
- Introduction to the Replica Theory of Disordered Statistical Systems
- Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising
- Typical reconstruction limits for distributed compressed sensing based on ℓ2,1-norm minimization and Bayesian optimal reconstruction
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Decoding by Linear Programming
- Information, Physics, and Computation
- Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem
- Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
- Statistical Physics of Spin Glasses and Information Processing
- Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation
- Subspace Methods for Joint Sparse Recovery
- Compressive MUSIC: Revisiting the Link Between Compressive Sensing and Array Signal Processing
- Model-Based Compressive Sensing
- Sparse solutions to linear inverse problems with multiple measurement vectors
- Compressed sensing
This page was built for publication: Typical reconstruction performance for distributed compressed sensing based on ℓ2,1-norm regularized least square and Bayesian optimal reconstruction: influences of noise
