Expectation-Maximization Gaussian-Mixture Approximate Message Passing

When recovering a sparse signal from noisy compressive linear measurements, the distribution of the signal's non-zero coefficients can have a profound effect on recovery mean-squared error (MSE). If this distribution was a priori known, then one could use computationally efficient approximate m...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 61; H. 19; S. 4658 - 4672
Hauptverfasser:	Vila, Jeremy P., Schniter, Philip
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.10.2013 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Applied sciences Approximation Approximation algorithms belief propagation Complexity theory Compressed sensing Detection, estimation, filtering, equalization, prediction Exact sciences and technology expectation maximization algorithms Gaussian mixture model Information, signal and communications theory Mathematical models Maximization Message passing Noise Noise measurement Reconstruction Recovery Run time (computers) Sampling, quantization Sensors Signal and communications theory Signal, noise Telecommunications and information theory Vectors Performance evaluation State of the art belief propagation Mixture theory Implementation Gaussian mixture model Mean square error Credal approach Gaussian process Message passing Minimax method A priori estimation Signal processing expectation maximization algorithms Bayes methods EM algorithm Compressed sensing
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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