Publications related to Hard Thresholding with Norm Constraints

Jump-Sparse and Sparse Recovery Using Potts Functionals

We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and pr ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Efficient Subnetwork Selection in Relay Networks

Christina Fragouli, Siddhartha Brahma, Ayan Sengupta

We consider a source that would like to communicate with a destination over a layered Gaussian relay network. We present a computationally efficient method that enables to select a near-optimal (in terms of throughput) subnetwork of a given size connecting ...

2014

Efficient Subnetwork Selection in Relay Networks

Christina Fragouli, Siddhartha Brahma, Ayan Sengupta

We consider a source that would like to communicate with a destination over a layered Gaussian relay network. We present a computationally efficient method that enables to select a near-optimal (in terms of throughput) subnetwork of a given size connecting ...

Ieee2014

Geometric Variational Models for Inverse Problems in Imaging

Virginia Estellers Casas

This dissertation develops geometric variational models for different inverse problems in imaging that are ill-posed, designing at the same time efficient numerical algorithms to compute their solutions. Variational methods solve inverse problems by the fo ...

EPFL2013

Sparse projections onto the simplex

Volkan Cevher, Christoph Koch, Anastasios Kyrillidis

Most learning methods with rank or sparsity constraints use convex relaxations, which lead to optimization with the nuclear norm or the`1-norm. However, several important learning applications cannot benet from this approach as they feature these convex no ...

JMLR W&CP2013

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider the problem of recovering a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model that can efficiently restrict the degrees of freedom of the problem and is generic enough to ...

Institute of Electrical and Electronics Engineers2012

Bearing estimation via spatial sparsity using compressive sensing

Volkan Cevher

Bearing estimation algorithms obtain only a small number of direction of arrivals (DOAs) within the entire angle domain, when the sources are spatially sparse. Hence, we propose a method to specifically exploit this spatial sparsity property. The method us ...

Institute of Electrical and Electronics Engineers2012

NoRSE: noise reduction and state evaluator for high-frequency single event traces

Ardemis Anoush Boghossian

Summary: NoRSE was developed to analyze high-frequency datasets collected from multistate, dynamic expts., such as mol. adsorption and desorption onto carbon nanotubes. As technol. improves sampling frequency, these stochastic datasets become increasingly ...

2012

On Accelerated Hard Thresholding Methods for Sparse Approximation

Volkan Cevher

We propose and analyze acceleration schemes for hard thresholding methods with applications to sparse approximation in linear inverse systems. Our acceleration schemes fuse combinatorial, sparse projection algorithms with convex optimization algebra to pro ...

Spie-Int Soc Optical Engineering, Po Box 10, Bellingham, Wa 98227-0010 Usa2011

On Variable Density Compressive Sampling

Pierre Vandergheynst, Yves Wiaux, Gilles Puy

We advocate an optimization procedure for variable density sampling in the context of compressed sensing. In this perspective, we introduce a minimization problem for the coherence between the sparsity and sensing bases, whose solution provides an optimize ...

Institute of Electrical and Electronics Engineers2011

Hard Thresholding with Norm Constraints

Graph Chatbot

Chat with Graph Search

Jump-Sparse and Sparse Recovery Using Potts Functionals

Efficient Subnetwork Selection in Relay Networks

Efficient Subnetwork Selection in Relay Networks

Geometric Variational Models for Inverse Problems in Imaging

Sparse projections onto the simplex

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Bearing estimation via spatial sparsity using compressive sensing

NoRSE: noise reduction and state evaluator for high-frequency single event traces

On Accelerated Hard Thresholding Methods for Sparse Approximation

On Variable Density Compressive Sampling

Efficient Subnetwork Selection in Relay Networks

Geometric Variational Models for Inverse Problems in Imaging

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Jump-Sparse and Sparse Recovery Using Potts Functionals

On Variable Density Compressive Sampling

NoRSE: noise reduction and state evaluator for high-frequency single event traces

Sparse projections onto the simplex

Bearing estimation via spatial sparsity using compressive sensing

Efficient Subnetwork Selection in Relay Networks

On Accelerated Hard Thresholding Methods for Sparse Approximation