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Proximal gradient methods for learning

Known as: Proximal gradient 
Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which… 
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Related topics

Related topics

19 relations
Convex analysisConvex conjugateConvex functionConvex optimization

Broader (1)

Machine learning

Papers overview

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2018
2018
Efficient Algorithm for Convolutional Dictionary Learning via Accelerated Proximal Gradient Consensus
Convolutional sparse representations are receiving an increase attention as a better alternative to the standard patch-based… 
2018
2018
Structural Local Sparse Tracking Method Based on Multi-feature Fusion and Fractional Differential
. In view of incomplete using single feature to describe the objects of interest and effectiveness of local sparse representation… 
2017
2017
Visual Tracking Via Sparse Representation With Reliable Structure Constraint
In this letter, we present a novel visual tracking algorithm based on sparse representation. In contrast to just use the target… 
Review
2017
Review
2017
Image deblurring in the presence of salt-and-pepper noise
This work addresses image recovery problem in the presence of salt-and-pepper noise and image blur. The salt-and-pepper noise… 
2016
2016
When Sensor Meets Tensor: Filling Missing Sensor Values Through a Tensor Approach
In the era of the Internet of Things, enormous number of sensors have been deployed in different locations, generating massive… 
2016
2016
Training Ternary Neural Networks with Exact Proximal Operator
In this paper, we propose a stochastic proximal gradient method to train ternary weight neural networks (TNN). The proposed… 
2014
2014
Data-driven scene understanding by adaptive exemplar retrieval
This article studies a data-driven approach for semantically scene understanding, without pixelwise annotation and classifier pre… 
2014
2014
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings
State of the art statistical estimators for high-dimensional problems take the form of regularized, and hence non-smooth, convex… 
2013
2013
Randomized Block Coordinate Non-Monotone Gradient Method for a Class of Nonlinear Programming
In this paper we propose a randomized block coordinate non-monotone gradient (RBCNMG) method for minimizing the sum of a smooth… 
Highly Cited
2010
Highly Cited
2010
Optimum Subspace Learning and Error Correction for Tensors
Confronted with the high-dimensional tensor-like visual data, we derive a method for the decomposition of an observed tensor into… 
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