Publications related to Distributed Sensing of Signals Linked by Sparse Filtering

Exploratory Analysis of Endogeneity in Discrete Choice Models

Michel Bierlaire, Anna Fernández Antolín, Amanda Blomberg Stathopoulos

A model is said to be affected by endogeneity when its deterministic part is correlated with the error term. This is an issue that affects both linear models such as regression and non-linear models like discrete choice models. It is a classical and well s ...

2014

Distributed Compressed Representation of Correlated Image Sets

Vijayaraghavan Thirumalai

Vision sensor networks and video cameras find widespread usage in several applications that rely on effective representation of scenes or analysis of 3D information. These systems usually acquire multiple images of the same 3D scene from different viewpoin ...

EPFL2012

Compressive Sampling Strategies for Multichannel Signals

Mohammad Golbabaee

Over the past decade researches in applied mathematics, signal processing and communications have introduced compressive sampling (CS) as an alternative to the Shannon sampling theorem. The two key observations making CS theory widely applicable to numerou ...

EPFL2012

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

Thresholding-based reconstruction of compressed correlated signals

Pascal Frossard, Alhussein Fawzi, Tamara Tosic

We consider the problem of recovering a set of correlated signals (\emph{e.g.,} images from different viewpoints) from a few linear measurements per signal. We assume that each sensor in a network acquires a compressed signal in the form of linear measurem ...

2012

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Matthias Seeger

Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher- ...

2011

Dense Disparity Estimation from Linear Measurements

Pascal Frossard, Vijayaraghavan Thirumalai

This paper proposes a methodology to estimate the correlation model between a pair of images that are given under the form of linear measurements. We consider an image pair whose common objects are relatively displaced due to the positioning of vision sens ...

2011

Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements

Pierre Vandergheynst, Mohammad Golbabaee

Assume a multichannel data matrix, which due to the column-wise dependencies, has low-rank and joint-sparse representation. This matrix wont have many degrees of freedom. Enormous developments over the last decade in areas of compressed sensing and low-ran ...

2011

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Matthias Seeger

Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher- ...

2010

Compressive Sensing of Localized Signals: Application to Analog-to-Information Conversion

Juri Ranieri

Compressed sensing hinges on the sparsity of signals to allow their reconstruction starting from a limited number of measures. When reconstruction is possible, the SNR of the reconstructed signal depends on the energy collected in the acquisition. Hence, i ...

2010

Distributed Sensing of Signals Linked by Sparse Filtering

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Exploratory Analysis of Endogeneity in Discrete Choice Models

Distributed Compressed Representation of Correlated Image Sets

Compressive Sampling Strategies for Multichannel Signals

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Thresholding-based reconstruction of compressed correlated signals

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Dense Disparity Estimation from Linear Measurements

Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Compressive Sensing of Localized Signals: Application to Analog-to-Information Conversion

Distributed Compressed Representation of Correlated Image Sets

Exploratory Analysis of Endogeneity in Discrete Choice Models

Dense Disparity Estimation from Linear Measurements

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Compressive Sampling Strategies for Multichannel Signals

Compressive Sensing of Localized Signals: Application to Analog-to-Information Conversion

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Thresholding-based reconstruction of compressed correlated signals

Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements