Covariance-Free Sparse Bayesian Learning

Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem while also providing uncertainty quantification. The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a lar...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 70; S. 3818 - 3831
Hauptverfasser:	Lin, Alexander, Song, Andrew H., Bilgic, Berkin, Ba, Demba
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Bayes methods Bayesian analysis Coding compressed sensing computational efficiency Computational modeling Covariance matrices Covariance matrix Encoding expectation-maximization algorithms Inference Inference algorithms Linear algebra Linear systems Machine learning Mathematical analysis Signal processing algorithms Sparse Bayesian learning (SBL)
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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