Publications related to Compressive sensing meets game theory

LAYOUT ASSESSMENT METHOD AND SYSTEM

Frank Grégoire Jean de Morsier, Virginia Estellers Casas, Nathalie Casati, Maria Gabrani

Candidate layout patterns can be assessed using a sparse pattern dictionary of known design layout patterns by determining sparse coefficients for each candidate pattern, reconstructing the respective candidate pattern, and determining reconstruction error ...

U.S. Patent and Trademark Office; U.S. DEPARTMENT OF COMMERCE2013

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider the problem of recovering a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model that can efficiently restrict the degrees of freedom of the problem and is generic enough to ...

Institute of Electrical and Electronics Engineers2012

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider recovery of a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model which can efficiently restricts the degrees of freedom of data and, at the same time, is generic so that f ...

2012

Compressible distributions for high-dimensional statistics

Volkan Cevher, Rémi Gribonval

We develop a principled way of identifying probability distributions whose independent and identically distributed realizations are compressible, i.e., can be well approximated as sparse. We focus on Gaussian compressed sensing, an example of underdetermin ...

Institute of Mathematical Statistics2012

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

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Pascal Frossard, Pierre Vandergheynst, Adel Rahmoune

This paper introduces a novel algorithm for sparse approximation in redundant dictionaries called the M-term pursuit (MTP). This algorithm decomposes a signal into a linear combination of atoms that are selected in order to represent the main signal compon ...

Institute of Electrical and Electronics Engineers2012

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

An Alps View of Sparse Recovery

Volkan Cevher

We provide two compressive sensing (CS) recovery algorithms based on iterative hard-thresholding. The algorithms, collectively dubbed as algebraic pursuits (ALPS), exploit the restricted isometry properties of the CS measurement matrix within the algebra o ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Online performance guarantees for sparse recovery

Volkan Cevher

A K*-sparse vector x* ∈ RN produces measurements via linear dimensionality reduction as u = Φx* +n, where Φ ∈ RM×N (M

2011

Structured Sparsity Models for Compressively Sensed Electrocardiogram Signals: A Comparative Study

David Atienza Alonso, Pierre Vandergheynst, Nadia Khaled, Hossein Mamaghanian

We have recently quantified and validated the potential of the emerging compressed sensing (CS) paradigm for real-time energy-efficient electrocardiogram (ECG) compression on resource-constrained sensors. In the present work, we investigate applying sparsi ...

2011

Compressive sensing meets game theory

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LAYOUT ASSESSMENT METHOD AND SYSTEM

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Compressible distributions for high-dimensional statistics

Compressive Sampling Strategies for Multichannel Signals

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

An Alps View of Sparse Recovery

Online performance guarantees for sparse recovery

Structured Sparsity Models for Compressively Sensed Electrocardiogram Signals: A Comparative Study

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Compressive Sampling Strategies for Multichannel Signals

Compressible distributions for high-dimensional statistics

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

An Alps View of Sparse Recovery

Structured Sparsity Models for Compressively Sensed Electrocardiogram Signals: A Comparative Study

LAYOUT ASSESSMENT METHOD AND SYSTEM

Online performance guarantees for sparse recovery