One sketch for all: fast algorithms for compressed sensing
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
A coordinate gradient descent method for nonsmooth separable minimization
Mathematical Programming: Series A and B
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
A comparative study of quantized compressive sensing schemes
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
A single-letter characterization of optimal noisy compressed sensing
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Coherence analysis of iterative thresholding algorithms
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Performance analysis of support recovery with joint sparsity constraints
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Bayesian compressive sensing via belief propagation
IEEE Transactions on Signal Processing
Variance-component based sparse signal reconstruction and model selection
IEEE Transactions on Signal Processing
An adaptive greedy algorithm with application to nonlinear communications
IEEE Transactions on Signal Processing
Model-based compressive sensing
IEEE Transactions on Information Theory
Shannon-theoretic limits on noisy compressive sampling
IEEE Transactions on Information Theory
Greedy sparse signal reconstruction from sign measurements
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Iterative signal recovery from incomplete samples: technical perspective
Communications of the ACM
CoSaMP: iterative signal recovery from incomplete and inaccurate samples
Communications of the ACM
Analysis of orthogonal matching pursuit using the restricted isometry property
IEEE Transactions on Information Theory
ADMiRA: atomic decomposition for minimum rank approximation
IEEE Transactions on Information Theory
Performance analysis for sparse support recovery
IEEE Transactions on Information Theory
Randomization of data acquisition and l1-optimization (recognition with compression)
Automation and Remote Control
A non-adapted sparse approximation of PDEs with stochastic inputs
Journal of Computational Physics
A robust and efficient algorithm for distributed compressed sensing
Computers and Electrical Engineering
Improved Bounds on Restricted Isometry Constants for Gaussian Matrices
SIAM Journal on Matrix Analysis and Applications
Sparse and silent coding in neural circuits
Neurocomputing
A* orthogonal matching pursuit: Best-first search for compressed sensing signal recovery
Digital Signal Processing
Coherence Pattern-Guided Compressive Sensing with Unresolved Grids
SIAM Journal on Imaging Sciences
Adaptive Compressed Image Sensing Using Dictionaries
SIAM Journal on Imaging Sciences
Hard Thresholding Pursuit: An Algorithm for Compressive Sensing
SIAM Journal on Numerical Analysis
Fast k-selection algorithms for graphics processing units
Journal of Experimental Algorithmics (JEA)
Environmental monitoring via compressive sensing
Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data
Journal of Approximation Theory
SCoBeP: Dense image registration using sparse coding and belief propagation
Journal of Visual Communication and Image Representation
Fractal pursuit for compressive sensing signal recovery
Computers and Electrical Engineering
Digital paparazzi: spotting celebrities in professional photo libraries
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Greedy sparsity-constrained optimization
The Journal of Machine Learning Research
Compressed Sensing via Dimension Spread in Dimension-Restricted Systems
Wireless Personal Communications: An International Journal
Compressed sensing signal recovery via forward-backward pursuit
Digital Signal Processing
Online Dictionary Learning Based Intra-frame Video Coding
Wireless Personal Communications: An International Journal
Matrix Recipes for Hard Thresholding Methods
Journal of Mathematical Imaging and Vision
Hi-index | 755.18 |
We propose a new method for reconstruction of sparse signals with and without noisy perturbations, termed the subspace pursuit algorithm. The algorithm has two important characteristics: low computational complexity, comparable to that of orthogonal matching pursuit techniques when applied to very sparse signals, and reconstruction accuracy of the same order as that of linear programming (LP) optimization methods. The presented analysis shows that in the noiseless setting, the proposed algorithm can exactly reconstruct arbitrary sparse signals provided that the sensing matrix satisfies the restricted isometry property with a constant parameter. In the noisy setting and in the case that the signal is not exactly sparse, it can be shown that the mean-squared error of the reconstruction is upper-bounded by constant multiples of the measurement and signal perturbation energies.