Robust sensing of low-rank matrices with non-orthogonal sparse decomposition
From MaRDI portal
Publication:6051152
DOI10.1016/j.acha.2023.06.004arXiv2103.05523OpenAlexW3134932577MaRDI QIDQ6051152
Publication date: 19 September 2023
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2103.05523
Cites Work
- Unnamed Item
- Unnamed Item
- Low rank matrix recovery from rank one measurements
- Robust sparse phase retrieval made easy
- A mathematical introduction to compressive sensing
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- Iterative hard thresholding for compressed sensing
- On gradients of functions definable in o-minimal structures
- Global error bounds for piecewise convex polynomials
- Sparse power factorization: balancing peakiness and sample complexity
- Dimension reduction by random hyperplane tessellations
- NP-hardness and inapproximability of sparse PCA
- Robust recovery of low-rank matrices with non-orthogonal sparse decomposition from incomplete measurements
- Convex Recovery of a Structured Signal from Independent Random Linear Measurements
- Conditions on optimal support recovery in unmixing problems by means of multi-penalty regularization
- One-Bit Compressed Sensing by Linear Programming
- Minimization of multi-penalty functionals by alternating iterative thresholding and optimal parameter choices
- Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices
- Blind Recovery of Sparse Signals From Subsampled Convolution
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- 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
- High-Dimensional Probability
- The phase retrieval problem
- Jointly low-rank and bisparse recovery: Questions and partial answers
- Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
- Regularization and Variable Selection Via the Elastic Net
- Upper and Lower Bounds for Stochastic Processes
- The Łojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems
- Low-rank matrix completion using alternating minimization
This page was built for publication: Robust sensing of low-rank matrices with non-orthogonal sparse decomposition