Sparse reconstruction with multiple Walsh matrices

DOI10.1515/apam-2017-0047zbMath1455.94045arXiv1804.04335OpenAlexW2963485101WikidataQ129735391 ScholiaQ129735391MaRDI QIDQ2317908

Enrico Au-Yeung

Publication date: 13 August 2019

Published in: Advances in Pure and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1804.04335

zbMATH Keywords

random matricessignal processingsparse reconstruction

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Random matrices (algebraic aspects) (15B52) Numerical linear algebra (65F99)

Cites Work

• A mathematical introduction to compressive sensing
• Sparse Legendre expansions via \(\ell_1\)-minimization
• Iterative hard thresholding for compressed sensing
• Random sampling of sparse trigonometric polynomials
• The restricted isometry property and its implications for compressed sensing
• A simple proof of the restricted isometry property for random matrices
• Uniform uncertainty principle for Bernoulli and subgaussian ensembles
• Majorizing measures: The generic chaining
• Sparse Recovery Algorithms: Sufficient Conditions in Terms of Restricted Isometry Constants
• Counting faces of randomly projected polytopes when the projection radically lowers dimension
• Compressed sensing and best 𝑘-term approximation
• New and Improved Johnson–Lindenstrauss Embeddings via the Restricted Isometry Property
• On sparse reconstruction from Fourier and Gaussian measurements
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Greed is Good: Algorithmic Results for Sparse Approximation
• Just relax: convex programming methods for identifying sparse signals in noise
• From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
• Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
• Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation
• Model-Based Compressive Sensing
• Stable signal recovery from incomplete and inaccurate measurements
• Compressed sensing
• Unnamed Item
• Unnamed Item