Book Review: A mathematical introduction to compressive sensing
From MaRDI portal
Publication:3178754
DOI10.1090/bull/1546zbMath1352.00019OpenAlexW2512213236MaRDI QIDQ3178754
Publication date: 20 December 2016
Published in: Bulletin of the American Mathematical Society (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1090/bull/1546
Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to information and communication theory (94-01) External book reviews (00A17)
Related Items
A new conjugate gradient hard thresholding pursuit algorithm for sparse signal recovery, Stable high-order cubature formulas for experimental data, Generalized Sparse Bayesian Learning and Application to Image Reconstruction, Unnamed Item, Sequential image recovery using joint hierarchical Bayesian learning, Performance bounds of the intensity-based estimators for noisy phase retrieval, Greedy variance estimation for the LASSO
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- Derandomizing restricted isometries via the Legendre symbol
- A mathematical introduction to compressive sensing
- The Gelfand widths of \(\ell_p\)-balls for \(0 < p \leq 1\)
- Explicit constructions of RIP matrices and related problems
- User-friendly tail bounds for sums of random matrices
- From Steiner formulas for cones to concentration of intrinsic volumes
- Minimax risk over \(l_ p\)-balls for \(l_ q\)-error
- Templates for convex cone problems with applications to sparse signal recovery
- An uncertainty principle for cyclic groups of prime order
- Some remarks on greedy algorithms
- The convex geometry of linear inverse problems
- How well can we estimate a sparse vector?
- Universality in polytope phase transitions and message passing algorithms
- The Littlewood-Offord problem and invertibility of random matrices
- Suprema of Chaos Processes and the Restricted Isometry Property
- Uncertainty Principles and Signal Recovery
- Counting faces of randomly projected polytopes when the projection radically lowers dimension
- Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Stable recovery of sparse overcomplete representations in the presence of noise
- Recovery of Exact Sparse Representations in the Presence of Bounded Noise
- Greed is Good: Algorithmic Results for Sparse Approximation
- Just relax: convex programming methods for identifying sparse signals in noise
- Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing
- Near-optimal sparse fourier representations via sampling
- Fast, small-space algorithms for approximate histogram maintenance
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Linear Inversion of Band-Limited Reflection Seismograms
- Signal Recovery and the Large Sieve
- Atomic Decomposition by Basis Pursuit
- Universal approximation bounds for superpositions of a sigmoidal function
- Learning Decision Trees Using the Fourier Spectrum
- Uncertainty principles and ideal atomic decomposition
- The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding versus Dynamic Range
- Data compression and harmonic analysis
- Sparse Approximate Solutions to Linear Systems
- Randomized Interpolation and Approximation of Sparse Polynomials
- Entropy-based algorithms for best basis selection
- Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
- Living on the edge: phase transitions in convex programs with random data
- Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
- Compressed sensing
- Compressed sensing
