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GENERALIZED SPARSE SIGNAL MIXING MODEL AND APPLICATION TO NOISY BLIND SOURCE SEPARATION
GENERALIZED SPARSE SIGNAL MIXING MODEL NOISY BLIND SOURCE SEPARATION
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2015/9/29
Sparse constraints on signal decompositions are justified bytypical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to ...
Multi-Sparse Signal Recovery for Compressive Sensing
Multi-Sparse Signal Recovery Compressive Sensing Information Theory
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2012/6/19
Signal recovery is one of the key techniques of Compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one do...
Adaptive Sensing Performance Lower Bounds for Sparse Signal Estimation and Testing
adaptive sensing minimax lower bounds sequential experimental design sparsity-based models
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2012/6/19
This paper gives a precise characterization of the fundamental limits of adaptive sensing for diverse estimation and testing problems concerning sparse signals. We consider in particular the setting i...
Templates for Convex Cone Problems with Applications to Sparse Signal Recovery
Optimal rst-order methods Nesterov's accelerated descent algorithms
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2010/12/3
This paper develops a general framework for solving a variety of convex cone problems that
frequently arise in signal processing, machine learning, statistics, and other elds. The approach works as ...