Publications related to Sparse Distributed Learning Based on Diffusion Adaptation

Joint Low-rank and Sparse Light Field Modeling for Dense Multiview Data Compression

Pierre Vandergheynst, Mahdad Hosseini Kamal

The effective representation of the structures in the multiview images is an important problem that arises in visual sensor networks. This paper presents a novel recovery scheme from compressive samples which exploit local and non-local correlated structur ...

2013

Joint Low-Rank And Sparse Light Field Modelling For Dense Multiview Data Compression

Pierre Vandergheynst, Mahdad Hosseini Kamal

The effective representation of the structures in the multiview images is an important problem that arises in visual sensor networks. This paper presents a novel recovery scheme from compressive samples which exploit local and non-local correlated structur ...

Ieee2013

Sparse diffusion LMS for distributed adaptive estimation

Ali H. Sayed

The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, ...

IEEE2012

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Jean-Charles Baritaux

The recent advances in fluorescent molecular probes, photon detection instrumentation, and photon propagation models in tissue, have facilitated the emergence of innovative molecular imaging technologies such as Fluorescence Diffuse Optical Tomography (FDO ...

EPFL2012

Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks

Ali H. Sayed

We propose an adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes. The cost function is assumed to consist of a collection of individual components. Diffusion adaptation allows the nodes to coopera ...

IEEE2012

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

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

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider recovery of a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model which can efficiently restricts the degrees of freedom of data and, at the same time, is generic so that f ...

2012

Compressible distributions for high-dimensional statistics

Volkan Cevher, Rémi Gribonval

We develop a principled way of identifying probability distributions whose independent and identically distributed realizations are compressible, i.e., can be well approximated as sparse. We focus on Gaussian compressed sensing, an example of underdetermin ...

Institute of Mathematical Statistics2012

Compressive Sampling Strategies for Multichannel Signals

Mohammad Golbabaee

Over the past decade researches in applied mathematics, signal processing and communications have introduced compressive sampling (CS) as an alternative to the Shannon sampling theorem. The two key observations making CS theory widely applicable to numerou ...

EPFL2012

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

Sparse Distributed Learning Based on Diffusion Adaptation

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Joint Low-rank and Sparse Light Field Modeling for Dense Multiview Data Compression

Joint Low-Rank And Sparse Light Field Modelling For Dense Multiview Data Compression

Sparse diffusion LMS for distributed adaptive estimation

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks

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

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

Compressible distributions for high-dimensional statistics

Compressive Sampling Strategies for Multichannel Signals

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Joint Low-rank and Sparse Light Field Modeling for Dense Multiview Data Compression

Joint Low-Rank And Sparse Light Field Modelling For Dense Multiview Data Compression

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Sparse diffusion LMS for distributed adaptive estimation

Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks

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

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

Compressible distributions for high-dimensional statistics

Compressive Sampling Strategies for Multichannel Signals

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm