Publications related to Sparse matrix

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

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

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

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

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Benjamin Hamner

Sparse methods are widely used in image and audio processing for denoising and classification, but there have been few previous applications to neural signals for brain-computer interfaces (BCIs). We used the dictionary- learning algorithm K-SVD, coupled w ...

2011

Distributed sensor failure detection in sensor networks

Pascal Frossard, Nikolaos Thomos, Tamara Tosic

We investigate the problem of distributed sensors' failure detection in networks with a small number of defective sensors, whose measurements differ significantly from neighboring sensor measurements. Defective sensors are represented by non-zero values in ...

2011

Estimating Sparse MIMO Channels Having Common Support

Martin Vetterli, Ali Hormati, Yann Barbotin

We propose an algorithm (SCS-FRI) to estimate multipath channels with Sparse Common Support (SCS) based on Finite Rate of Inno- vation (FRI) sampling. In this setup, theoretical lower-bounds are derived, and simulation in a Rayleigh fading environment show ...

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

Compressive sensing meets game theory

Volkan Cevher, Sina Jafarpour

We introduce the Multiplicative Update Selector and Estimator (MUSE) algorithm for sparse approximation in under-determined linear regression problems. Given ƒ = Φα* + μ, the MUSE provably and efficiently finds a k-sparse vector α̂ such that ∥Φα̂ − ƒ∥∞ ≤ ∥ ...

IEEE2011

Multilevel Schwarz methods for elliptic partial differential equations

Alfio Quarteroni, Giovanni Migliorati

We investigate multilevel Schwarz domain decomposition preconditioners, to efficiently solve linear systems arising from numerical discretizations of elliptic partial differential equations by the finite element method. In our analysis we deal with unstruc ...

Elsevier2011

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Sparse matrix

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Online performance guarantees for sparse recovery

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

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

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

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

Distributed sensor failure detection in sensor networks

Estimating Sparse MIMO Channels Having Common Support

Compressive sensing meets game theory

Multilevel Schwarz methods for elliptic partial differential equations

Graph Chatbot

Chat with Graph Search

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

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

Distributed sensor failure detection in sensor networks

Compressive sensing meets game theory

Multilevel Schwarz methods for elliptic partial differential equations

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Online performance guarantees for sparse recovery

Estimating Sparse MIMO Channels Having Common Support

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

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