Jointly low-rank and bisparse recovery: Questions and partial answers
From MaRDI portal
Publication:5220065
DOI10.1142/S0219530519410094zbMath1434.65050arXiv1902.04731WikidataQ126831598 ScholiaQ126831598MaRDI QIDQ5220065
Holger Rauhut, Laurent Jacques, Simon Foucart, Rémi Gribonval
Publication date: 10 March 2020
Published in: Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1902.04731
compressive sensingsample complexityrestricted isometry propertiesiterative thresholding algorithmshead and tail projectionsrank-one measurementssimultaneity of structures
Computational methods for sparse matrices (65F50) Numerical methods for low-rank matrix approximation; matrix compression (65F55)
Related Items
Robust sensing of low-rank matrices with non-orthogonal sparse decomposition, Riemannian thresholding methods for row-sparse and low-rank matrix recovery, Sampling schemes and recovery algorithms for functions of few coordinate variables, Hierarchical compressed sensing
Uses Software
Cites Work
- Low rank matrix recovery from rank one measurements
- Robust sparse phase retrieval made easy
- A mathematical introduction to compressive sensing
- Approximation of functions of few variables in high dimensions
- Sparse power factorization: balancing peakiness and sample complexity
- Sampling schemes and recovery algorithms for functions of few coordinate variables
- ROP: matrix recovery via rank-one projections
- NP-hardness and inapproximability of sparse PCA
- Low rank tensor recovery via iterative hard thresholding
- Iterative hard thresholding for low-rank recovery from rank-one projections
- PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming
- Approximation Algorithms for Model-Based Compressive Sensing
- Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices
- Sparse recovery with pre-Gaussian random matrices
- A Tight Bound of Hard Thresholding
- Near-Optimal Compressed Sensing of a Class of Sparse Low-Rank Matrices Via Sparse Power Factorization
- Near-optimal estimation of simultaneously sparse and low-rank matrices from nested linear measurements
- Stable low-rank matrix recovery via null space properties
- Inexact Gradient Projection and Fast Data Driven Compressed Sensing
- Almost-polynomial ratio ETH-hardness of approximating densest k-subgraph
- Reliable Recovery of Hierarchically Sparse Signals for Gaussian and Kronecker Product Measurements
- Sampling and Reconstructing Signals From a Union of Linear Subspaces
- Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements