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Some Recovery Conditions for Basis Learning by L1-Minimization

Publications associées (41)

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Matrix ALPS: Accelerated Low Rank and Sparse Matrix Reconstruction

Volkan Cevher, Anastasios Kyrillidis

We propose Matrix ALPS for recovering a sparse plus low-rank decomposition of a matrix given its corrupted and incomplete linear measurements. Our approach is a first-order projected gradient method over non-convex sets, and it exploits a well-known memory ...

IEEE2012

Optimal image alignment with random projections of manifolds: algorithm and geometric analysis

Pascal Frossard, Daniel Kressner, Effrosyni Kokiopoulou

This paper addresses the problem of image alignment based on random measurements. Image alignment consists of estimating the relative transformation between a query image and a reference image. We consider the specific problem where the query image is prov ...

2011

A Fast Wavelet-Based Reconstruction Method for Magnetic Resonance Imaging

Michaël Unser, Matthieu Guerquin-Kern

In this work, we exploit the fact that wavelets can represent magnetic resonance images well, with relatively few coefficients. We use this property to improve magnetic resonance imaging (MRI) reconstructions from undersampled data with arbitrary k-space t ...

Institute of Electrical and Electronics Engineers2011

Spread Spectrum for Universal Compressive Sampling

Pierre Vandergheynst, Yves Wiaux, Rémi Gribonval, Gilles Puy

We propose a universal and efficient compressive sampling strategy based on the use of a spread spectrum technique. The method essentially consists in a random pre-modulation of the signal of interest followed by projections onto randomly selected vectors ...

2011

N° 8 Tumbleweed 1er éd.

Dieter Dietz, Aline Dubach

Tumbleweed is an architectural artifact that engages the private with the public, the exterior with the interior and the ideal with the real in a mirroring effect: the terms of their relationships are multiplied and transformed into a new understanding… ...

2011

Fast hard thresholding with Nesterov's gradient method

Volkan Cevher, Sina Jafarpour

We provide an algorithmic framework for structured sparse recovery which unifies combinatorial optimization with the non-smooth convex optimization framework by Nesterov [1, 2]. Our algorithm, dubbed Nesterov iterative hard-thresholding (NIHT), is similar ...

2010

Geometry-aware analysis of high-dimensional visual information sets

Effrosyni Kokiopoulou

Over the past few decades we have been experiencing a data explosion; massive amounts of data are increasingly collected and multimedia databases, such as YouTube and Flickr, are rapidly expanding. At the same time rapid technological advancements in mobil ...

EPFL2009

Sparsity & [and] dictionaries - algorithms & [and] design

Karin Schnass

With the flood of information available today the question how to deal with high dimensional data/signals, which are cumbersome to handle, to calculate with and to store, is highly important. One approach to reducing this flood is to find sparse signal rep ...

EPFL2009

A note on the representation of parametric models for multivariate extremes

Marc-Olivier Boldi

In this note, the representations of extremal Dirichlet and logistic distributions are reviewed and extended. These new representations allow exact simulations of the spectral distribution functions and an extension of the extremal logistic case to dimensi ...

2009