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Average case analysis of sparse recovery with Thresholding: New bounds based on average dictionary coherence

Related publications (48)

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Optimal Sampling Rates in Infinite-Dimensional Compressed Sensing

Martin Vetterli, Loïc Arnaud Baboulaz, Mitra Fatemi

The theory of compressed sensing studies the problem of recovering a high dimensional sparse vector from its projections onto lower dimensional subspaces. The recently introduced framework of infinite-dimensional compressed sensing [1], to some extent gene ...

2013

We examine the problem of image registration when images have a sparse representation in a dictionary of geometric features. We propose a novel algorithm for aligning images by pairing their sparse components. We show numerically that this algorithm works ...

2013

Person Independent 3D Gaze Estimation From Remote RGB-D Cameras

Jean-Marc Odobez, Kenneth Alberto Funes Mora

We address the problem of person independent 3D gaze estimation using a remote, low resolution, RGB-D camera. The approach relies on a sparse technique to reconstruct normalized eye test images from a gaze appearance model (a set of eye image/gaze pairs) a ...

IEEE2013

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

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In bus communications methods and apparatus, a first set of physical signals representing the information to be conveyed over the bus is provided, and mapped to a codeword of a sparse signaling code, wherein a codeword is representable as a vector of a plu ...

2012

For linear models, compressed sensing theory and methods enable recovery of sparse signals of interest from few measurements-in the order of the number of nonzero entries as opposed to the length of the signal of interest. Results of similar flavor have mo ...

Institute of Electrical and Electronics Engineers2012

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We address the problem of microphone location cali- bration where the sensor positions have a sparse spatial approximation on a discretized grid. We characterize the microphone signals as a sparse vector represented over a codebook of multi-channel signals ...

2012

Sparse diffusion LMS for distributed adaptive estimation

Ali H. Sayed

The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, ...

IEEE2012

Structured Sparse Coding for Microphone Array Location Calibration

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We address the problem of microphone location calibration from a sparse coding perspective where the sensor positions are approximated over a discretized grid. We characterize the microphone signals as a sparse vector represented over a codebook of multi-c ...

2012

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