SIAM Journal on Control and Optimization
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
On-line learning and stochastic approximations
On-line learning in neural networks
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Outcomes of the equivalence of adaptive ridge with least absolute shrinkage
Proceedings of the 1998 conference on Advances in neural information processing systems II
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Convergence Rates in Forward--Backward Splitting
SIAM Journal on Optimization
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
A statistical framework for genomic data fusion
Bioinformatics
Algorithms for simultaneous sparse approximation: part I: Greedy pursuit
Signal Processing - Sparse approximations in signal and image processing
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
Method of optimal directions for frame design
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05
Uncovering shared structures in multiclass classification
Proceedings of the 24th international conference on Machine learning
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
Classification of arrayCGH data using fused SVM
Bioinformatics
Sparse Bayesian nonparametric regression
Proceedings of the 25th international conference on Machine learning
The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms
Proceedings of the 25th international conference on Machine learning
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
Consistency of Trace Norm Minimization
The Journal of Machine Learning Research
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
A coordinate gradient descent method for nonsmooth separable minimization
Mathematical Programming: Series A and B
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches
ECML '07 Proceedings of the 18th European conference on Machine Learning
Convex multi-task feature learning
Machine Learning
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with structured sparsity
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Group lasso with overlap and graph lasso
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Stochastic methods for l1 regularized loss minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows
International Journal of Computer Vision
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
The Journal of Machine Learning Research
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
IEEE Transactions on Information Theory
Recovering sparse signals with a certain family of nonconvex penalties and DC programming
IEEE Transactions on Signal Processing
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Model-based compressive sensing
IEEE Transactions on Information Theory
Solving structured sparsity regularization with proximal methods
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
The Journal of Machine Learning Research
Variable Sparsity Kernel Learning
The Journal of Machine Learning Research
Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity
PRNI '11 Proceedings of the 2011 IEEE International Workshop on Pattern Recognition in NeuroImaging
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
The Journal of Machine Learning Research
Proximal Methods for Hierarchical Sparse Coding
The Journal of Machine Learning Research
Convex and Network Flow Optimization for Structured Sparsity
The Journal of Machine Learning Research
Structured Variable Selection with Sparsity-Inducing Norms
The Journal of Machine Learning Research
Structured sparsity in structured prediction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Foundations and Trends® in Machine Learning
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Sparse Bayesian learning for basis selection
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework
IEEE Transactions on Signal Processing
Efficient discriminative projections for compact binary descriptors
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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
Sparse localized deformation components
ACM Transactions on Graphics (TOG)
Sparse projections of medical images onto manifolds
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Attribute-efficient evolvability of linear functions
Proceedings of the 5th conference on Innovations in theoretical computer science
Proceedings of the 7th ACM international conference on Web search and data mining
Supervised feature selection in graphs with path coding penalties and network flows
The Journal of Machine Learning Research
An Evaluation of the Sparsity Degree for Sparse Recovery with Deterministic Measurement Matrices
Journal of Mathematical Imaging and Vision
Sparse iterative closest point
SGP '13 Proceedings of the Eleventh Eurographics/ACMSIGGRAPH Symposium on Geometry Processing
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Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate nonsmooth norms. The goal of this monograph is to present from a general perspective optimization tools and techniques dedicated to such sparsity-inducing penalties. We cover proximal methods, block-coordinate descent, reweighted l2-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions, and provide an extensive set of experiments to compare various algorithms from a computational point of view.