Model selection with low complexity priors

Regularization plays a pivotal role when facing the challenge of solving ill-posed inverse problems, where the number of observations is smaller than the ambient dimension of the object to be estimated. A line of recent work has studied regularization models with various types of low-dimensional str...

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Veröffentlicht in:	Information and inference 2015-09, Vol.4 (3), p.230-287
Hauptverfasser:	Vaiter, Samuel, Golbabaee, Mohammad, Fadili, Jalal, Peyré, Gabriel
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science Image Processing Information Theory Mathematics Optimization and Control Statistics Statistics Theory
Online-Zugang:	Volltext
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