| Metadato | Etiqueta | Valor | Idioma |
|---|---|---|---|
| dc.contributor.author | Autor | Giannakis, Georgios B | es |
| dc.contributor.author | Autor | Mateos, Gonzalo | es |
| dc.contributor.author | Autor | Bazerque, Juan Andrés | es |
| dc.date.accessioned | Fecha ingreso | 2023-12-11T19:57:48Z | - |
| dc.date.available | Fecha disponible | 2023-12-11T19:57:48Z | - |
| dc.date.issued | Año de publicación | 2013 | es |
| dc.date.submitted | Fecha de subida | 20231211 | es |
| dc.description | Descripción | Trabajo presentado a 2013 IEEE International Conference on Acoustics, Speech and Signal Processing | es |
| dc.description.abstract | Resumen | A novel regularizer capturing the tensor rank is introduced in this paper as the key enabler for completion of three-way data arrays with missing entries. The novel regularized imputation approach induces sparsity in the factors of the tensor's PARAFAC decomposition, thus reducing its rank. The focus is on count processes which emerge in diverse applications ranging from genomics to computer and social networking. Based on Poisson count data, a maximum aposteriori (MAP) estimator is developed using the Kullback-Leibler divergence criterion. This probabilistic approach also facilitates incorporation of correlated priors regularizing the rank, while endowing the tensor imputation method with extra smoothing and prediction capabilities. Tests on simulated and real datasets corroborate the sparsifying regularization effect, and demonstrate recovery of 15% missing RNA-sequencing data with an inference error of -12dB | es |
| dc.identifier.citation | Citación | Bazerque, J.A., Mateos, G y Giannakis, G.B. "Inference of Poisson count processes using low-rank tensor data" Publicado en: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 2013, pp. 5989-5993, doi: 10.1109/ICASSP.2013.6638814. | es |
| dc.identifier.uri | URI | https://hdl.handle.net/20.500.12008/41795 | - |
| dc.language | metadata.dc.language | en | es |
| dc.rights | Derechos | 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.rights.licence | Licencia | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) | es |
| dc.subject | Palabras clave | Tensor | es |
| dc.subject | Palabras clave | Low-rank | es |
| dc.subject | Palabras clave | Missing data | es |
| dc.subject | Palabras clave | Bayesian inference | es |
| dc.subject | Palabras clave | Poisson process | es |
| dc.subject.other | Descriptores | Sistemas y Control | es |
| dc.title | Título | Inference of poisson count processes using low-rank tensor data | es |
| dc.type | Tipo de documento | Ponencia | es |
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| BMG13a.pdf | — | 286.85 KB | Adobe PDF |