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A low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries

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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

Near-Optimal Bayesian Localization via Incoherence and Sparsity

Volkan Cevher

This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework for the localization problem and provide sparse approximations to its optimal ...

2009

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In recent years, many works on geometric image representation have appeared in the literature. Geometric video representation has not received such an important attention so far, and only some initial works in the area have been presented. Works on geometr ...

2009

This article presents an alteration of greedy algorithms like thresholding or (Orthogonal) Matching Pursuit which improves their performance in finding sparse signal representations in redundant dictionaries. These algorithms can be split into a sensing an ...

2008

On finding approximate nearest neighbours in a set of compressible signals

Pierre Vandergheynst, Philippe Jost

Numerous applications demand that we manipulate large sets of very high-dimensional signals. A simple yet common example is the problem of finding those signals in a database that are closest to a query. In this paper, we tackle this problem by restricting ...

2008

Algorithms and methods for sparse approximation in structured dictionaries

Lorenzo Peotta

In the last decade we observed an increasing interaction between data compression and sparse signals approximations. Sparse approximations are desirable because they compact the energy of the signals in few elements and correspond to a structural simplific ...

EPFL2007

Algorithmic aspects of sparse approximations

Philippe Jost

Typical tasks in signal processing may be done in simpler ways or more efficiently if the signals to analyze are represented in a proper way. This thesis deals with some algorithmic problems related to signal approximation, more precisely, in the novel fie ...

EPFL2007

Matching Pursuit with Block Incoherent Dictionaries

Pierre Vandergheynst, Lorenzo Peotta

There has been an intense activity recently in the field of sparse approximations with redundant dictionaries, largely motivated by the practical performances of algorithms such as Matching Pursuit and Basis Pursuit. However, most of the theoretical result ...

2007

Blind Audio-Visual Source Separation Using Sparse Redundant Representations

Pierre Vandergheynst, Gianluca Monaci, Anna Llagostera Casanovas

This report presents a new method to confront the Blind Audio Source Separation (BASS) problem, by means of audio and visual information. In a given mixture, we are able to locate the video sources first and, posteriorly, recover each source signal, only w ...

2006

On the Use of A Priori Information for Sparse Signal Approximations

Pierre Vandergheynst, Lorenzo Granai, Oscar Divorra Escoda

Recent results have underlined the importance of incoherence in redundant dictionaries for a good behavior of decomposition algorithms like Matching and Basis Pursuits. However, appropriate dictionaries for a given application may not necessarily be able t ...

2006