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Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Bazerque, Juan Andrés | es |
dc.contributor.author | Mateos, Gonzalo | es |
dc.contributor.author | Giannakis, Georgios B | es |
dc.date.accessioned | 2023-12-11T19:57:45Z | - |
dc.date.available | 2023-12-11T19:57:45Z | - |
dc.date.issued | 2013 | es |
dc.date.submitted | 20231211 | es |
dc.identifier.citation | Bazerque, J.A., Mateos, G y Giannakis, G.B. "Rank Regularization and Bayesian Inference for Tensor Completion and Extrapolation," [Preprint] Publicado en: IEEE Transactions on Signal Processing, 2013, vol. 61, no. 22, pp. 5689-5703, doi: 10.1109/TSP.2013.2278516. | es |
dc.identifier.uri | https://hdl.handle.net/20.500.12008/41784 | - |
dc.description.abstract | A novel regularizer of the PARAFAC decomposition factors capturing the tensor's rank is proposed in this paper, as the key enabler for completion of three-way data arrays with missing entries. Set in a Bayesian framework, the tensor completion method incorporates prior information to enhance its smoothing and prediction capabilities. This probabilistic approach can naturally accommodate general models for the data distribution, lending itself to various fitting criteria that yield optimum estimates in the maximum-a-posteriori sense. In particular, two algorithms are devised for Gaussian- and Poisson-distributed data, that minimize the rank-regularized least-squares error and Kullback-Leibler divergence, respectively. The proposed technique is able to recover the “ground-truth” tensor rank when tested on synthetic data, and to complete brain imaging and yeast gene expression datasets with 50% and 15% of missing entries respectively, resulting in recovery errors at -11 dB and -15 dB | es |
dc.language | en | 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.subject | Tensor | es |
dc.subject | Low-rank | es |
dc.subject | Missing data | es |
dc.subject | Bayesian inference | es |
dc.subject | Poisson process | es |
dc.subject.other | Sistemas y Control | es |
dc.title | Rank regularization and bayesian inference for tensor completion and extrapolation | es |
dc.type | Preprint | es |
dc.rights.licence | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) | es |
Aparece en las colecciones: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
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