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Blind multiclass ensemble classification
dc.contributor.author | Traganitis, Panagiotis |
dc.contributor.author | Pagès Zamora, Alba Maria |
dc.contributor.author | Giannakis, Georgios B. |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2018-08-03T07:53:48Z |
dc.date.available | 2018-08-03T07:53:48Z |
dc.date.issued | 2018 |
dc.identifier.citation | Traganitis, P. A., Pagès-Zamora, A., Giannakis, G.B. Blind multiclass ensemble classification. "IEEE transactions on signal processing", 2018. |
dc.identifier.issn | 1053-587X |
dc.identifier.uri | http://hdl.handle.net/2117/120513 |
dc.description | © 2018 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 | The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims at such highperformance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a blind scheme for learning from ensembles of classifiers, using a moment matching method that leverages joint tensor and matrix factorization. Blind refers to the combiner who has no knowledge of the groundtruth labels that each classifier has been trained on. A rigorous performance analysis is derived and the proposed scheme is evaluated on synthetic and real datasets. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal |
dc.subject.lcsh | Signal processing |
dc.subject.other | Ensemble learning |
dc.subject.other | Unsupervised |
dc.subject.other | Multiclass classification |
dc.subject.other | Crowdsourcing |
dc.title | Blind multiclass ensemble classification |
dc.type | Article |
dc.subject.lemac | Tractament del senyal |
dc.contributor.group | Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions |
dc.identifier.doi | 10.1109/TSP.2018.2860562 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8421667/ |
dc.rights.access | Open Access |
local.identifier.drac | 23306508 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TEC2015-69648-REDC/ES/RED COMONSENS/ |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/2PE/TEC2016-75067-C4-2-R-CARMEN |
local.citation.author | Traganitis, P. A.; Pagès-Zamora, A.; Giannakis, G.B. |
local.citation.publicationName | IEEE transactions on signal processing |
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