Concept

Sparse matrix

Related publications (226)

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

-Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Pierre Vandergheynst, Mohammad Golbabaee

We propose a novel approach to reconstruct Hyperspectral images from very few number of noisy compressive measurements. Our reconstruction approach is based on a convex minimization which penalizes both the nuclear norm and the l(2,1) mixed-norm of the dat ...

Ieee2012

Sparse Gradient Image Reconstruction from Incomplete Fourier Measurements and Prior Edge Information

Jan Sickmann Hesthaven

In many imaging applications, such as functional Magnetic Resonance Imaging (fMRI), full, uniformly- sampled Cartesian Fourier (frequency space) measurements are acquired to reconstruct an image. In order to reduce scan time and increase temporal resolutio ...

2012

Assessing Sparse Coding Methods for Contextual Shape Indexing of Maya Hieroglyphs

Daniel Gatica-Perez, Jean-Marc Odobez, Edgar Roman Rangel

Bag-of-visual-words or bag-of-visterms (bov) is a common technique used to index Multimedia information with the purposes of retrieval and classification. In this work we address the problem of constructing efficient bov representations of complex shapes a ...

2012

Computing symmetric nonnegative rank factorizations

An algorithm is described for the nonnegative rank factorization (NRF) of some completely positive (CP) matrices whose rank is equal to their CP-rank. The algorithm can compute the symmetric NRF of any nonnegative symmetric rank-r matrix that contains a di ...

Elsevier2012

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

Reconstruction of Large, Irregularly Sampled Multidimensional Images. A Tensor-Based Approach

Michaël Unser

Many practical applications require the reconstruction of images from irregularly sampled data. The spline formalism offers an attractive framework for solving this problem; the currently available methods, however, are hard to deploy for large-scale inter ...

IEEE2011

Sparse Source Recovery from Boundary Measurements using an Extension of Prony's Method for the Poisson Equation

Djano Kandaswamy

Electroencephalography (EEG) is a key modality to monitor brain activity with high temporal resolution. EEG makes use of an array of electrodes to measure the electrical potential on the scalp. While most traditional EEG analyses have looked at EEG rhythms ...

EPFL2011

On 1-norm stochastic optimal control with bounded control inputs

Milan Korda

This paper deals with the finite horizon stochastic optimal control problem with the expectation of the 1-norm as the objective function and jointly Gaussian, although not necessarily independent, disturbances. We develop an approximation strategy that sol ...

2011

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei, Mohammadjavad Taghizadeh

We study the sparsity of spectro-temporal representation of speech in reverberant acoustic conditions. This study motivates the use of structured sparsity models for efficient recovery of speech. We formulate the underdetermined convolutive speech separati ...

Idiap2011

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