Publications related to Learning with Structured Sparsity: From Discrete to Convex and Back.

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Pierre Vandergheynst, Mahdad Hosseini Kamal

We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensio ...

2014

Jump-Sparse and Sparse Recovery Using Potts Functionals

Martin Kurt Storath

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

Metric Learning with Rank and Sparsity Constraints

Volkan Cevher, Baran Gözcü, Bubacarr Bah

Choosing a distance preserving measure or metric is fun- damental to many signal processing algorithms, such as k- means, nearest neighbor searches, hashing, and compressive sensing. In virtually all these applications, the efficiency of the signal process ...

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

Image reconstruction of non-planar scenes from compressed multi-view measurements

Pierre Vandergheynst, Gilles Puy

We extend the domain of application of a method developed recently for joint reconstruction of l images, representing the same scene, from few multi-view measurements. While this method was initially designed for planar scenes, we show here that parametric ...

2013

Time-Optimal Path Following for Robots With Convex-Concave Constraints Using Sequential Convex Programming

Quoc Tran Dinh

Time-optimal path following considers the problem of moving along a predetermined geometric path in minimum time. In the case of a robotic manipulator with simplified constraints, a convex reformulation of this optimal control problem has been derived prev ...

Institute of Electrical and Electronics Engineers2013

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadra ...

2013

l0-deconvolution for compressive diffusion MRI

Jean-Philippe Thiran, Yves Wiaux, Alessandro Daducci, Anna Auria Rasclosa

Diffusion MRI is a well established imaging modality providing a powerful way to non-invasively probe the structure of the white matter. Despite the potential of the technique, the intrinsic long scan times of these sequences have hampered their use in cli ...

2013

To Convexify or Not? Regression with Clustering Penalties on Graphs

Volkan Cevher, Luca Baldassarre, Marwa El Halabi

We consider minimization problems that are compositions of convex functions of a vector

\x \in\reals^N

with submodular set functions of its support (i.e., indices of the non-zero coefficients of

\x

). Such problems are in general difficult for large

N

...

IEEE2013

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

Learning with Structured Sparsity: From Discrete to Convex and Back.

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A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Jump-Sparse and Sparse Recovery Using Potts Functionals

Metric Learning with Rank and Sparsity Constraints

Geometric Variational Models for Inverse Problems in Imaging

Image reconstruction of non-planar scenes from compressed multi-view measurements

Time-Optimal Path Following for Robots With Convex-Concave Constraints Using Sequential Convex Programming

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

l0-deconvolution for compressive diffusion MRI

To Convexify or Not? Regression with Clustering Penalties on Graphs

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

Geometric Variational Models for Inverse Problems in Imaging

l0-deconvolution for compressive diffusion MRI

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Image reconstruction of non-planar scenes from compressed multi-view measurements

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

To Convexify or Not? Regression with Clustering Penalties on Graphs

Metric Learning with Rank and Sparsity Constraints

Jump-Sparse and Sparse Recovery Using Potts Functionals

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

Time-Optimal Path Following for Robots With Convex-Concave Constraints Using Sequential Convex Programming