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A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed [Formula Omitted] Norm

IEEE transactions on signal processing, 2009-01, Vol.57 (1), p.289

2009

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A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothedell {0} Norm

IEEE transactions on signal processing, 2009-01, Vol.57 (1), p.289-301

2009

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A new approach in decomposition over multiple-overcomplete dictionaries with application to image inpainting

2009 IEEE International Workshop on Machine Learning for Signal Processing, 2009, p.1-6

2009

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Two dimensional compressive classifier for sparse images

2009 16th IEEE International Conference on Image Processing (ICIP), 2009, p.2137-2140

2009

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Compressed sensing Block MAP-LMS adaptive filter for sparse channel estimation and a Bayesian Cramer-Rao bound

2009 IEEE International Workshop on Machine Learning for Signal Processing, 2009, p.1-6

2009

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Complex-valued sparse representation based on smoothed ℓ0 norm

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, p.3881-3884

2008

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Decoding real-field codes by an iterative Expectation-Maximization (EM) algorithm

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, p.3169-3172

2008

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Thresholded smoothed-ℓ0(SL0) dictionary learning for sparse representations

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, p.1825-1828

2009

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Inflating compressed samples: A joint source-channel coding approach for noise-resistant compressed sensing

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, p.2957-2960

2009

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ECG denoising and compression by sparse 2D separable transform with overcomplete mixed dictionaries

2009 IEEE International Workshop on Machine Learning for Signal Processing, 2009, p.1-6

2009

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A minimization-projection (MP) approach for blind separating convolutive mixtures

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol.5, p.V-533

2004

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How to use real-valued sparse recovery algorithms for complex-valued sparse recovery?

2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012, p.849-853

2012

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A projected gradient-based algorithm to unmix hyperspectral data

2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012, p.2482-2486

2012

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Blind source separation of discrete finite alphabet sources using a single mixture

2011 IEEE Statistical Signal Processing Workshop (SSP), 2011, p.709-712

2011

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Error correction via smoothed L0-norm recovery

2011 IEEE Statistical Signal Processing Workshop (SSP), 2011, p.289-292

2011

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Watermarking Based on Independent Component Analysis in Spatial Domain

2011 UkSim 13th International Conference on Computer Modelling and Simulation, 2011, p.299-303

2011

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The 2016 Signal Separation Evaluation Campaign

Latent Variable Analysis and Signal Separation, 2017, p.323-332

2017

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k/K-Nearest Neighborhood Criterion for Improving Locally Linear Embedding

2009 Sixth International Conference on Computer Graphics, Imaging and Visualization, 2009, p.392-397

2009

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Two Dimensional Compressive Classifier for Sparse Images

2009 Sixth International Conference on Computer Graphics, Imaging and Visualization, 2009, p.402-405

2009

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Source Estimation in Noisy Sparse Component Analysis

2007 15th International Conference on Digital Signal Processing, 2007, p.219-222

2007

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A first step to convolutive sparse representation

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, p.1921-1924

2008

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Adaptive Sparse Source Separation with Application to Speech Signals

2007 IEEE International Conference on Signal Processing and Communications, 2007, p.640-643

2007

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Monoaural Audio Source Separation Using Deep Convolutional Neural Networks

Latent Variable Analysis and Signal Separation, p.258-266

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Estimating the mixing matrix in Sparse Component Analysis (SCA) based on multidimensional subspace clustering

2007 IEEE International Conference on Telecommunications and Malaysia International Conference on Communications, 2007, p.670-675

2007

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Toward Rank Disaggregation: An Approach Based on Linear Programming and Latent Variable Analysis

Latent Variable Analysis and Signal Separation, p.192-200

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A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed [Formula Omitted] Norm

A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothedell {0} Norm

A new approach in decomposition over multiple-overcomplete dictionaries with application to image inpainting

Two dimensional compressive classifier for sparse images

Compressed sensing Block MAP-LMS adaptive filter for sparse channel estimation and a Bayesian Cramer-Rao bound

Complex-valued sparse representation based on smoothed ℓ0 norm

Decoding real-field codes by an iterative Expectation-Maximization (EM) algorithm

Thresholded smoothed-ℓ0(SL0) dictionary learning for sparse representations

Inflating compressed samples: A joint source-channel coding approach for noise-resistant compressed sensing

ECG denoising and compression by sparse 2D separable transform with overcomplete mixed dictionaries

A minimization-projection (MP) approach for blind separating convolutive mixtures

How to use real-valued sparse recovery algorithms for complex-valued sparse recovery?

A projected gradient-based algorithm to unmix hyperspectral data

Blind source separation of discrete finite alphabet sources using a single mixture

Error correction via smoothed L0-norm recovery

Watermarking Based on Independent Component Analysis in Spatial Domain

The 2016 Signal Separation Evaluation Campaign

k/K-Nearest Neighborhood Criterion for Improving Locally Linear Embedding

Two Dimensional Compressive Classifier for Sparse Images

Source Estimation in Noisy Sparse Component Analysis

A first step to convolutive sparse representation

Adaptive Sparse Source Separation with Application to Speech Signals

Monoaural Audio Source Separation Using Deep Convolutional Neural Networks

Estimating the mixing matrix in Sparse Component Analysis (SCA) based on multidimensional subspace clustering

Toward Rank Disaggregation: An Approach Based on Linear Programming and Latent Variable Analysis

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