Compressive data gathering for large-scale wireless sensor networks
Proceedings of the 15th annual international conference on Mobile computing and networking
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
False data injection attacks against state estimation in electric power grids
Proceedings of the 16th ACM conference on Computer and communications security
Stagewise weak gradient pursuits
IEEE Transactions on Signal Processing
An iterative Bayesian algorithm for sparse component analysis in presence of noise
IEEE Transactions on Signal Processing
Parametric dictionary design for sparse coding
IEEE Transactions on Signal Processing
Double sparsity: learning sparse dictionaries for sparse signal approximation
IEEE Transactions on Signal Processing
Bayesian compressive sensing using Laplace priors
IEEE Transactions on Image Processing
Randomization of data acquisition and l1-optimization (recognition with compression)
Automation and Remote Control
SMALLbox - an evaluation framework for sparse representations and dictionary learning algorithms
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
False data injection attacks against state estimation in electric power grids
ACM Transactions on Information and System Security (TISSEC)
Sparse representation of deformable 3D organs with spherical harmonics and structured dictionary
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Practical data compression in wireless sensor networks: A survey
Journal of Network and Computer Applications
Matching Pursuits with random sequential subdictionaries
Signal Processing
Evolution-enhanced multiscale overcomplete dictionaries learning for image denoising
Engineering Applications of Artificial Intelligence
Dictionary learning based sparse coefficients for audio classification with max and average pooling
Digital Signal Processing
Fractal pursuit for compressive sensing signal recovery
Computers and Electrical Engineering
Information Sciences: an International Journal
Sensor selection via compressed sensing
Automatica (Journal of IFAC)
Hi-index | 35.70 |
Sparse signal approximations have become a fundamental tool in signal processing with wide-ranging applications from source separation to signal acquisition. The ever-growing number of possible applications and, in particular, the ever-increasing problem sizes now addressed lead to new challenges in terms of computational strategies and the development of fast and efficient algorithms has become paramount. Recently, very fast algorithms have been developed to solve convex optimization problems that are often used to approximate the sparse approximation problem; however, it has also been shown, that in certain circumstances, greedy strategies, such as orthogonal matching pursuit, can have better performance than the convex methods. In this paper, improvements to greedy strategies are proposed and algorithms are developed that approximate orthogonal matching pursuit with computational requirements more akin to matching pursuit. Three different directional optimization schemes based on the gradient, the conjugate gradient, and an approximation to the conjugate gradient are discussed, respectively. It is shown that the conjugate gradient update leads to a novel implementation of orthogonal matching pursuit, while the gradient-based approach as well as the approximate conjugate gradient methods both lead to fast approximations to orthogonal matching pursuit, with the approximate conjugate gradient method being superior to the gradient method.