Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem

In this paper, we develop a Bayesian evidence maximization framework to solve the sparse non-negative least squares (S-NNLS) problem. We introduce a family of probability densities referred to as the rectified Gaussian scale mixture (R-GSM) to model the sparsity enforcing prior distribution for the...

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Veröffentlicht in:	IEEE transactions on signal processing 2018-06, Vol.66 (12), p.3124-3139
Hauptverfasser:	Nalci, Alican, Fedorov, Igor, Al-Shoukairi, Maher, Liu, Thomas T., Rao, Bhaskar D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayes methods Estimation GSM Magnetic resonance imaging Matching pursuit algorithms Mathematical model Non-negative least squares rectified Gaussian scale mixtures Signal processing algorithms sparse Bayesian learning sparse signal recovery
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