Publications related to Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

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

Structured Sparse Coding for Microphone Array Location Calibration

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

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

Matrix ALPS: Accelerated Low Rank and Sparse Matrix Reconstruction

Volkan Cevher, Anastasios Kyrillidis

We propose Matrix ALPS for recovering a sparse plus low-rank decomposition of a matrix given its corrupted and incomplete linear measurements. Our approach is a first-order projected gradient method over non-convex sets, and it exploits a well-known memory ...

IEEE2012

Equivalence of synthesis and atomic formulations of sparse recovery

Volkan Cevher, Mitra Fatemi, Shayan Dashmiz

Recovery of sparse signals from linear, dimensionality reducing measurements broadly fall under two well-known formulations, named the synthesis and the analysis a ́ la Elad et al. Recently, Chandrasekaran et al. introduced a new algorithmic sparse recover ...

Ieee2012

TamaRISC-CS: An Ultra-Low-Power Application-Specific Processor for Compressed Sensing

David Atienza Alonso, Andreas Peter Burg, Pascal Andreas Meinerzhagen, Jeremy Hugues-Felix Constantin, Ahmed Yasir Dogan, Oskar Andersson

Compressed sensing (CS) is a universal technique for the compression of sparse signals. CS has been widely used in sensing platforms where portable, autonomous devices have to operate for long periods of time with limited energy resources. Therefore, an ul ...

2012

On Variable Density Compressive Sampling

Pierre Vandergheynst, Yves Wiaux, Gilles Puy

We advocate an optimization procedure for variable density sampling in the context of compressed sensing. In this perspective, we introduce a minimization problem for the coherence between the sparsity and sensing bases, whose solution provides an optimize ...

Institute of Electrical and Electronics Engineers2011

Compressive Sampling of Multiple Sparse Signals Having Common Support Using Finite Rate of Innovation Principles

Martin Vetterli, Ali Hormati

In sensor networks and communication systems, one is often confronted with sampling multiple sparse signals having a common support set. Multipath channels in a multiple-input multiple-output (MIMO) wireless communication setting is an interesting example ...

Institute of Electrical and Electronics Engineers2011

Low complexity RST invariant image recognition using Fourier Mellin transform

Pierre-André Farine, Sara Grassi Pauletti, Pradyumna Ayyalasomayajula

In this paper we propose a low complexity method for Rotation, Scale and Translation (RST) invariant content-based image retrieval, suitable for a handheld image recognition device. The RST compensation method is based on Fourier-Mellin Transform (FMT) whi ...

EURASIP2011

Spread Spectrum for Universal Compressive Sampling

Pierre Vandergheynst, Yves Wiaux, Rémi Gribonval, Gilles Puy

We propose a universal and efficient compressive sampling strategy based on the use of a spread spectrum technique. The method essentially consists in a random pre-modulation of the signal of interest followed by projections onto randomly selected vectors ...

2011

Deliverable 3.1: Report on Learning Structured Sparse Models

The aim of this work package (WP) is to explore approaches to learn structured sparse models, that is sparse models where the sparsity assumption seems not to be sufficient, or when there is hope to exploit some additional knowledge together with the spars ...

2011

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

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Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Structured Sparse Coding for Microphone Array Location Calibration

Matrix ALPS: Accelerated Low Rank and Sparse Matrix Reconstruction

Equivalence of synthesis and atomic formulations of sparse recovery

TamaRISC-CS: An Ultra-Low-Power Application-Specific Processor for Compressed Sensing

On Variable Density Compressive Sampling

Compressive Sampling of Multiple Sparse Signals Having Common Support Using Finite Rate of Innovation Principles

Low complexity RST invariant image recognition using Fourier Mellin transform

Spread Spectrum for Universal Compressive Sampling

Deliverable 3.1: Report on Learning Structured Sparse Models

On Variable Density Compressive Sampling

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Equivalence of synthesis and atomic formulations of sparse recovery

Spread Spectrum for Universal Compressive Sampling

Compressive Sampling of Multiple Sparse Signals Having Common Support Using Finite Rate of Innovation Principles

Deliverable 3.1: Report on Learning Structured Sparse Models

Structured Sparse Coding for Microphone Array Location Calibration

TamaRISC-CS: An Ultra-Low-Power Application-Specific Processor for Compressed Sensing

Matrix ALPS: Accelerated Low Rank and Sparse Matrix Reconstruction

Low complexity RST invariant image recognition using Fourier Mellin transform