L Regularization: A Thresholding Representation Theory and a Fast Solver

The special importance of L_{1/2} regularization has been recognized in recent studies on sparse modeling (particularly on compressed sensing). The L_{1/2} regularization, however, leads to a nonconvex, nonsmooth, and non-Lipschitz optimization problem that is difficult to solve fast and efficiently...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 23; H. 7; S. 1013 - 1027
Hauptverfasser:	Xu, Zongben, Chang, Xiangyu, Xu, Fengmin, Zhang, Hai
Format:	Journal Article
Sprache:	Englisch Japanisch
Veröffentlicht:	IEEE 01.07.2012
Schlagworte:	Compressed sensing Compressive sensing Convergence Convex functions half hard Iterative algorithms L_{q} regularization Learning systems Noise Optimization Signal processing algorithms soft sparsity thresholding algorithms thresholding representation theory
ISSN:	2162-237X, 2162-2388
Online-Zugang:	Volltext
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