Theoretical guarantees for graph sparse coding
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Publication:778037
DOI10.1016/j.acha.2019.03.003zbMath1448.94155OpenAlexW2931006236WikidataQ128102175 ScholiaQ128102175MaRDI QIDQ778037
Yael Yankelevsky, Michael Elad
Publication date: 30 June 2020
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.acha.2019.03.003
signal recoverymanifold learningorthogonal matching pursuitgraph regularizationbasis pursuitsparse representationsgraph sparse coding
Computational methods for sparse matrices (65F50) Applications of mathematical programming (90C90) Sensitivity, stability, parametric optimization (90C31) Source coding (94A29)
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Cites Work
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms
- Local and nonlocal discrete regularization on weighted graphs for image and mesh processing
- Algorithms for simultaneous sparse approximation. I: Greedy pursuit
- Algorithms for simultaneous sparse approximation. II: Convex relaxation
- Diffusion maps
- On Lebesgue-type inequalities for greedy approximation
- Atomic Decomposition by Basis Pursuit
- Compressed Sensing: How Sharp Is the Restricted Isometry Property?
- Decoding by Linear Programming
- Stable recovery of sparse overcomplete representations in the presence of noise
- Greed is Good: Algorithmic Results for Sparse Approximation
- Just relax: convex programming methods for identifying sparse signals in noise
- Sparse and Redundant Representations
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- Structured Compressed Sensing: From Theory to Applications
- RIP-Based Near-Oracle Performance Guarantees for SP, CoSaMP, and IHT
- Prediction of Partially Observed Dynamical Processes Over Networks via Dictionary Learning
- Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
- Progressive Image Denoising Through Hybrid Graph Laplacian Regularization: A Unified Framework
- A General Framework for Regularized, Similarity-Based Image Restoration
- Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding
- Finding GEMS: Multi-Scale Dictionaries For High-Dimensional Graph Signals
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Robust Recovery of Signals From a Structured Union of Subspaces
- Image Super-Resolution Via Sparse Representation
- Graph Regularized Sparse Coding for Image Representation
- Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
- Learning with Structured Sparsity
- Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
- Convergence Properties of the Spline Fit
