IEEE Trans Image Process - Universal regularizers for robust sparse coding and modeling.

Tópicos

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{ problem(2511) optim(1539) algorithm(950) }
{ use(976) code(926) identifi(902) }
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{ algorithm(1844) comput(1787) effici(935) }
{ data(3963) clinic(1234) research(1004) }
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{ method(1969) cluster(1462) data(1082) }
{ care(1570) inform(1187) nurs(1089) }
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{ model(3404) distribut(989) bayesian(671) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
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Resumo

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding theory, we propose a framework for designing sparsity regularization terms which have theoretical and practical advantages when compared with the more standard l(0) or l(1) ones. The presentation of the framework and theoretical foundations is complemented with examples that show its practical advantages in image denoising, zooming and classification.

Resumo Limpo

spars data model data assum well repres linear combin element dictionari gain consider attent recent year use led stateoftheart result mani signal imag process task now well understood choic sparsiti regular term critic success model base codelength minim interpret spars code use tool univers code theori propos framework design sparsiti regular term theoret practic advantag compar standard l l one present framework theoret foundat complement exampl show practic advantag imag denois zoom classif

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