Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Journal of Optimization Theory and Applications
A D. C. Optimization Algorithm for Solving the Trust-Region Subproblem
SIAM Journal on Optimization
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
A continuous approach for the concave cost supply problem via DC programming and DCA
Discrete Applied Mathematics
Surrogate maximization/minimization algorithms and extensions
Machine Learning
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
The Journal of Machine Learning Research
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
Just relax: convex programming methods for identifying sparse signals in noise
IEEE Transactions on Information Theory
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Optimization with Sparsity-Inducing Penalties
Foundations and Trends® in Machine Learning
Computational Statistics & Data Analysis
Modeling disease progression via fused sparse group lasso
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-source learning with block-wise missing data for Alzheimer's disease prediction
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A fast algorithm for nonconvex approaches to sparse recovery problems
Signal Processing
Multi-task regression using minimal penalties
The Journal of Machine Learning Research
Sparse high-dimensional fractional-norm support vector machine via DC programming
Computational Statistics & Data Analysis
A comparison of typical ℓp minimization algorithms
Neurocomputing
An SDP approach for ℓ0-minimization: Application to ARX model segmentation
Automatica (Journal of IFAC)
A reweighted nuclear norm minimization algorithm for low rank matrix recovery
Journal of Computational and Applied Mathematics
| Hi-index | 35.69 |
This paper considers the problem of recovering a sparse signal representation according to a signal dictionary. This problem could be formalized as a penalized least-squares problem in which sparsity is usually induced by a l1-norm penalty on the coefficients. Such an approach known as the Lasso or Basis Pursuit Denoising has been shown to perform reasonably well in some situations. However, it was also proved that nonconvex penalties like the pseudo lq-norm with q