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Distributed Sensing of Signals Linked by Sparse Filtering

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Sensing and Recovery under Sparsity Constraints

Ali Hormati

Popular transforms, like the discrete cosine transform or the wavelet transform, owe their success to the fact that they promote sparsity. These transforms are capable of extracting the structure of a large class of signals and representing them by a few t ...

EPFL2010

Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

Laurent Jacques

The theory of Compressed Sensing (CS) is based on reconstructing sparse signals from random linear measurements. As measurement of continuous signals by digital devices always involves some form of quantization, in practice devices based on CS encoding mus ...

2009

Dequantizing Compressed Sensing with non-Gaussian Constraints

Laurent Jacques

2009

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

Martin Vetterli, Amin Karbasi, Ali Hormati

Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linear measurements. One of the main challenges in CS is to find the support of a sparse signal from a set of noisy observations. In the CS literature, several i ...

2009

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

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

Martin Vetterli, Amin Karbasi, Ali Hormati

2009

Bayesian Inference and Optimal Design in the Sparse Linear Model

Matthias Seeger

The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maximum a posteriori sparse solutions and neglect to represent posterior uncertain ...

Massachusetts Institute of Technology Press2008

Disseminating Streaming Data in a Dynamic Environment: an Adaptive and Cost-Based Approach

In a distributed stream processing system, streaming data are continuously disseminated from the sources to the distributed processing servers. To enhance the dissemination efficiency, these servers are typically organized into one or more dissemination tr ...

Springer-Verlag2008

Bayesian Inference for Sparse Generalized Linear Models

Matthias Seeger, Matthias Bethge

We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties ...

Springer2007

Sparse Approximation by Linear Programming using an L1 Data-Fidelity Term

Pierre Vandergheynst, Lorenzo Granai

This paper studies the problem of sparse signal approximation over redundant dictionaries. Our attention is focused on the minimization of a cost function where the error is measured by using the L1 norm, giving thus less importance to outliers. We show a ...

IEEE2005