Inference of poisson count processes using low-rank tensor data
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
| 2013 | |
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Tensor Low-rank Missing data Bayesian inference Poisson process Sistemas y Control |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/41795 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522939030994944 |
|---|---|
| author | Giannakis, Georgios B |
| author2 | Mateos, Gonzalo Bazerque, Juan Andrés |
| author2_role | author author |
| author_facet | Giannakis, Georgios B Mateos, Gonzalo Bazerque, Juan Andrés |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Giannakis, Georgios B Mateos, Gonzalo Bazerque, Juan Andrés |
| dc.date.accessioned.none.fl_str_mv | 2023-12-11T19:57:48Z |
| dc.date.available.none.fl_str_mv | 2023-12-11T19:57:48Z |
| dc.date.issued.es.fl_str_mv | 2013 |
| dc.date.submitted.es.fl_str_mv | 20231211 |
| dc.description.abstract.none.fl_txt_mv | 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 |
| dc.description.es.fl_txt_mv | Trabajo presentado a 2013 IEEE International Conference on Acoustics, Speech and Signal Processing |
| dc.identifier.citation.es.fl_str_mv | 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. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/41795 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Tensor Low-rank Missing data Bayesian inference Poisson process |
| dc.subject.other.es.fl_str_mv | Sistemas y Control |
| dc.title.none.fl_str_mv | Inference of poisson count processes using low-rank tensor data |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Trabajo presentado a 2013 IEEE International Conference on Acoustics, Speech and Signal Processing |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_6e9cff621dccbae52e0dbf70b4e52fd7 |
| identifier_str_mv | 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. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41795 |
| publishDate | 2013 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2023-12-11T19:57:48Z2023-12-11T19:57:48Z201320231211Bazerque, 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.https://hdl.handle.net/20.500.12008/41795Trabajo presentado a 2013 IEEE International Conference on Acoustics, Speech and Signal ProcessingA 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 -12dBMade available in DSpace on 2023-12-11T19:57:48Z (GMT). No. of bitstreams: 5 BMG13a.pdf: 293734 bytes, checksum: 244fc2b286dfacffab5b7b656f035d8b (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4244 bytes, checksum: 528b6a3c8c7d0c6e28129d576e989607 (MD5) Previous issue date: 2013enengLas 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)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)TensorLow-rankMissing dataBayesian inferencePoisson processSistemas y ControlInference of poisson count processes using low-rank tensor dataPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaGiannakis, Georgios BMateos, GonzaloBazerque, Juan 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- Universidad de la Repúblicafalse |
| spellingShingle | Inference of poisson count processes using low-rank tensor data Giannakis, Georgios B Tensor Low-rank Missing data Bayesian inference Poisson process Sistemas y Control |
| status_str | publishedVersion |
| title | Inference of poisson count processes using low-rank tensor data |
| title_full | Inference of poisson count processes using low-rank tensor data |
| title_fullStr | Inference of poisson count processes using low-rank tensor data |
| title_full_unstemmed | Inference of poisson count processes using low-rank tensor data |
| title_short | Inference of poisson count processes using low-rank tensor data |
| title_sort | Inference of poisson count processes using low-rank tensor data |
| topic | Tensor Low-rank Missing data Bayesian inference Poisson process Sistemas y Control |
| url | https://hdl.handle.net/20.500.12008/41795 |
