Sparse Recovery From Multiple Measurement Vectors Using Exponentiated Gradient Updates

In this letter, we address the problem of reconstructing the common nonzero support of multiple joint sparse vectors from their noisy and underdetermined linear measurements. The support recovery problem is formulated as the selection of nonnegative hyperparameters of a correlation-aware, joint spar...

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Veröffentlicht in:	IEEE signal processing letters Jg. 25; H. 10; S. 1485 - 1489
Hauptverfasser:	Khanna, Saurabh, Murthy, Chandra Ramabhadra
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Compressive sensing Computational geometry Convergence Convexity Covariance covariance matching Covariance matrices Divergence exponentiated gradient (EG) updates joint sparsity multiple measurement vectors Noise measurement Optimization Recovery Regularization Sensors Signal processing algorithms Size measurement Sparse matrices sparse recovery Symmetric matrices Von Neumann divergence
ISSN:	1070-9908, 1558-2361
Online-Zugang:	Volltext
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