Publications related to A simple test to check the optimality of a sparse signal approximation

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Benjamin Hamner

Sparse methods are widely used in image and audio processing for denoising and classification, but there have been few previous applications to neural signals for brain-computer interfaces (BCIs). We used the dictionary- learning algorithm K-SVD, coupled w ...

2011

Compressive sensing meets game theory

Volkan Cevher, Sina Jafarpour

We introduce the Multiplicative Update Selector and Estimator (MUSE) algorithm for sparse approximation in under-determined linear regression problems. Given ƒ = Φα* + μ, the MUSE provably and efficiently finds a k-sparse vector α̂ such that ∥Φα̂ − ƒ∥∞ ≤ ∥ ...

IEEE2011

Fast orthogonal sparse approximation algorithms over local dictionaries

Pierre Vandergheynst, Rémi Gribonval

In this work we present a new greedy algorithm for sparse approximation called LocOMP. LocOMP is meant to be run on local dictionaries made of atoms with much shorter supports than the signal length. This notably encompasses shift-invariant dictionaries an ...

2011

Ultrasound Tomography with Learned Dictionaries

Martin Vetterli, Pascal Frossard, Ivana Tosic, Ivana Jovanovic

We propose a new method for imaging sound speed in breast tissue from measurements obtained by ultrasound tomography (UST) scan- ners. Given the measurements, our algorithm finds a sparse image representation in an overcomplete dictionary that is adapted t ...

2010

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation

Rémi Gribonval, Karin Schnass

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via

\ell_1

-minimisation. The problem can also be seen as factorising a

\ddim \times \nsig

matrix $Y=(y_1 \ldots y_\nsig), , y_n\in \R^\ ...

Institute of Electrical and Electronics Engineers2010

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

Greedy dictionary selection for sparse representation

Volkan Cevher

We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse, we mean that only a few dictionary elements, compared to the ambient signal ...

2009

Spread spectrum for imaging techniques in radio interferometry

Pierre Vandergheynst, Yves Wiaux, Gilles Puy

We consider the probe of astrophysical signals through radio interferometers with small field of view and baselines with non-negligible and constant component in the pointing direction. In this context, the visibilities measured essentially identify with a ...

Wiley-Blackwell2009

Spread spectrum for imaging techniques in radio interferometry and strings detection

Yves Wiaux, Gilles Puy

This poster is a summary of recent work published in: Spread spectrum for imaging techniques in radio interferometry, Y. Wiaux, G. Puy, Y. Boursier, and P. Vandergheynst, Mon. Not. R. Astron. Soc., 2009, Preprint arXiv:0907.0944v1. We consider the probe of ...

2009

Compressive sensing recovery of spike trains using a structured sparsity model

Volkan Cevher

The theory of Compressive Sensing (CS) exploits a well-known concept used in signal compression – sparsity – to design new, efficient techniques for signal acquisition. CS theory states that for a length-N signal x with sparsity level K, M = O(K log(N/K)) ...

2009

A simple test to check the optimality of a sparse signal approximation

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Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

Compressive sensing meets game theory

Fast orthogonal sparse approximation algorithms over local dictionaries

Ultrasound Tomography with Learned Dictionaries

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation

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

Greedy dictionary selection for sparse representation

Spread spectrum for imaging techniques in radio interferometry

Spread spectrum for imaging techniques in radio interferometry and strings detection

Compressive sensing recovery of spike trains using a structured sparsity model

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation

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

Fast orthogonal sparse approximation algorithms over local dictionaries

Compressive sensing meets game theory

Ultrasound Tomography with Learned Dictionaries

Spread spectrum for imaging techniques in radio interferometry

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

Greedy dictionary selection for sparse representation

Compressive sensing recovery of spike trains using a structured sparsity model

Spread spectrum for imaging techniques in radio interferometry and strings detection