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Matrix completion and extrapolation via kernel regression
dc.contributor.author | Giménez Febrer, Pedro Juan |
dc.contributor.author | Pagès Zamora, Alba Maria |
dc.contributor.author | Giannakis, Georgios B. |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2020-01-14T15:10:06Z |
dc.date.available | 2020-01-14T15:10:06Z |
dc.date.issued | 2019-10-01 |
dc.identifier.citation | Gimenez, P.; Pagès-Zamora, A.; Giannakis, G.B. Matrix completion and extrapolation via kernel regression. "IEEE transactions on signal processing", 1 Octubre 2019, vol. 67, núm. 19, p. 5004-5017. |
dc.identifier.issn | 1053-587X |
dc.identifier.uri | http://hdl.handle.net/2117/174782 |
dc.description | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
dc.description.abstract | Matrix completion and extrapolation (MCEX) are dealt with here over reproducing kernel Hilbert spaces (RKHSs) in order to account for prior information present in the available data. Aiming at a fast and low-complexity solver, the task is formulated as one of kernel ridge regression. The resultant MCEX algorithm can also afford online implementation, while the class of kernel functions also encompasses several existing approaches to MC with prior information. Numerical tests on synthetic and real datasets show that the novel approach is faster than widespread methods such as alternating least-squares (ALS) or stochastic gradient descent (SGD), and that the recovery error is reduced, especially when dealing with noisy data. |
dc.format.extent | 14 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal |
dc.subject | Àrees temàtiques de la UPC::Ensenyament i aprenentatge::TIC's aplicades a l'educació::Ensenyament virtual (eLearning) |
dc.subject.lcsh | Signal processing |
dc.subject.lcsh | Distance education |
dc.subject.other | Matrix completion |
dc.subject.other | Extrapolation |
dc.subject.other | RKHS |
dc.subject.other | Kernel ridge regression |
dc.subject.other | Graphs |
dc.subject.other | Online learning |
dc.title | Matrix completion and extrapolation via kernel regression |
dc.type | Article |
dc.subject.lemac | Tractament del senyal |
dc.subject.lemac | Ensenyament a distància |
dc.contributor.group | Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions |
dc.identifier.doi | 10.1109/TSP.2019.2932875 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8786233 |
dc.rights.access | Open Access |
local.identifier.drac | 25839773 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/1PE/TEC2016-75067-C4-2-R |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TEC2015-69648-REDC/ES/RED COMONSENS/ |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/PRI2017-2019/2017 SGR 578 |
local.citation.author | Gimenez, P.; Pagès-Zamora, A.; Giannakis, G.B. |
local.citation.publicationName | IEEE transactions on signal processing |
local.citation.volume | 67 |
local.citation.number | 19 |
local.citation.startingPage | 5004 |
local.citation.endingPage | 5017 |
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