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Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Ramírez Paulino, Ignacio | es |
dc.contributor.author | Lecumberry, Federico | es |
dc.contributor.author | Sapiro, Guillermo | es |
dc.date.accessioned | 2023-08-01T20:33:18Z | - |
dc.date.available | 2023-08-01T20:33:18Z | - |
dc.date.issued | 2009 | es |
dc.date.submitted | 20230801 | es |
dc.identifier.citation | Ramírez Paulino, I, Lecumberry, F, Sapiro, G. “Universal priors for sparse modeling”. Proceedings of the 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Aruba, Dutch Antilles, 2009.. DOI: 10.1109/CAMSAP.2009.5413302 | es |
dc.identifier.uri | https://hdl.handle.net/20.500.12008/38679 | - |
dc.description.abstract | 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. In this work, we use tools from information theory to propose a sparsity regularization term which has several theoretical and practical advantages over the more standard ¿ 0 or ¿ 1 ones, and which leads to improved coding performance and accuracy in reconstruction tasks. We also briefly report on further improvements obtained by imposing low mutual coherence and Gram matrix norm on the learned dictionaries | es |
dc.language | en | es |
dc.publisher | IEEE | es |
dc.relation.ispartof | 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Aruba, Dutch Antilles, 2009. | es |
dc.rights | Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014) | es |
dc.title | Universal priors for sparse modeling | es |
dc.type | Ponencia | es |
dc.rights.licence | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) | es |
dc.identifier.doi | DOI: 10.1109/CAMSAP.2009.5413302 | es |
Aparece en las colecciones: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
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RLS09.pdf | 607,25 kB | Adobe PDF | Visualizar/Abrir |
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