Submatrices with NonUniformly Selected Random Supports and Insights into Sparse Approximation
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Publication:5006456
DOI10.1137/20M1386384zbMath1470.60015arXiv2012.02082OpenAlexW3191363167MaRDI QIDQ5006456
Publication date: 16 August 2021
Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2012.02082
Random matrices (probabilistic aspects) (60B20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Numerical computation of matrix norms, conditioning, scaling (65F35) Sampling theory in information and communication theory (94A20) Conditioning of matrices (15A12)
Cites Work
- Invertibility of random submatrices via tail-decoupling and a matrix Chernoff inequality
- User-friendly tail bounds for sums of random matrices
- Near-ideal model selection by \(\ell _{1}\) minimization
- On the conditioning of random subdictionaries
- Invertibility of ``large submatrices with applications to the geometry of Banach spaces and harmonic analysis
- Compressed sensing with structured sparsity and structured acquisition
- Norms of random submatrices and sparse approximation
- An Algorithm for Variable Density Sampling with Block-Constrained Acquisition
- BREAKING THE COHERENCE BARRIER: A NEW THEORY FOR COMPRESSED SENSING
- On Sparse Representations in Arbitrary Redundant Bases
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Recovery of Short, Complex Linear Combinations Via<tex>$ell _1$</tex>Minimization
- Dictionary Preconditioning for Greedy Algorithms
- Stable and Robust Sampling Strategies for Compressive Imaging
- Variable Density Sampling with Continuous Trajectories
- Uncertainty Relations and Sparse Signal Recovery for Pairs of General Signal Sets
- A Probabilistic and RIPless Theory of Compressed Sensing
- Asymptotic Theory of Rejective Sampling with Varying Probabilities from a Finite Population
- Monotone Convergence of Binomial Probabilities and a Generalization of Ramanujan's Equation
- Compressed sensing
