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dc.contributor.authorTraganitis, Panagiotis
dc.contributor.authorPagès Zamora, Alba Maria
dc.contributor.authorGiannakis, Georgios B.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2018-08-03T07:53:48Z
dc.date.available2018-08-03T07:53:48Z
dc.date.issued2018
dc.identifier.citationTraganitis, P. A., Pagès-Zamora, A., Giannakis, G.B. Blind multiclass ensemble classification. "IEEE transactions on signal processing", 2018.
dc.identifier.issn1053-587X
dc.identifier.urihttp://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.abstractThe 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.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
dc.subject.lcshSignal processing
dc.subject.otherEnsemble learning
dc.subject.otherUnsupervised
dc.subject.otherMulticlass classification
dc.subject.otherCrowdsourcing
dc.titleBlind multiclass ensemble classification
dc.typeArticle
dc.subject.lemacTractament del senyal
dc.contributor.groupUniversitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
dc.identifier.doi10.1109/TSP.2018.2860562
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8421667/
dc.rights.accessOpen Access
drac.iddocument23306508
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2015-69648-REDC
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/2PE/TEC2016-75067-C4-2-R - CARMEN
upcommons.citation.authorTraganitis, P. A., Pagès-Zamora, A., Giannakis, G.B.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameIEEE transactions on signal processing


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