Blind multiclass ensemble classification

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Authors: Traganitis, Panagiotis, Pagès Zamora, Alba Maria|||0000-0002-7087-7014, Giannakis, Georgios B.
Format: article
Publication Date:2018
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/120513
Online Access:https://hdl.handle.net/2117/120513
https://dx.doi.org/10.1109/TSP.2018.2860562
Access Level:Open access
Keyword:Signal processing
Ensemble learning
Unsupervised
Multiclass classification
Crowdsourcing
Tractament del senyal
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
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oai_identifier_str oai:upcommons.upc.edu:2117/120513
network_acronym_str ES
network_name_str España
repository_id_str
spelling Blind multiclass ensemble classificationTraganitis, PanagiotisPagès Zamora, Alba Maria|||0000-0002-7087-7014Giannakis, Georgios B.Signal processingEnsemble learningUnsupervisedMulticlass classificationCrowdsourcingTractament del senyalÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal© 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.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.Peer Reviewed20182018-01-0120182018-08-03journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/120513https://dx.doi.org/10.1109/TSP.2018.2860562reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 TEC2015-69648-REDC RED COMONSENSopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1205132026-05-27T15:37:01Z
dc.title.none.fl_str_mv Blind multiclass ensemble classification
title Blind multiclass ensemble classification
spellingShingle Blind multiclass ensemble classification
Traganitis, Panagiotis
Signal processing
Ensemble learning
Unsupervised
Multiclass classification
Crowdsourcing
Tractament del senyal
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
title_short Blind multiclass ensemble classification
title_full Blind multiclass ensemble classification
title_fullStr Blind multiclass ensemble classification
title_full_unstemmed Blind multiclass ensemble classification
title_sort Blind multiclass ensemble classification
dc.creator.none.fl_str_mv Traganitis, Panagiotis
Pagès Zamora, Alba Maria|||0000-0002-7087-7014
Giannakis, Georgios B.
author Traganitis, Panagiotis
author_facet Traganitis, Panagiotis
Pagès Zamora, Alba Maria|||0000-0002-7087-7014
Giannakis, Georgios B.
author_role author
author2 Pagès Zamora, Alba Maria|||0000-0002-7087-7014
Giannakis, Georgios B.
author2_role author
author
dc.subject.none.fl_str_mv Signal processing
Ensemble learning
Unsupervised
Multiclass classification
Crowdsourcing
Tractament del senyal
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
topic Signal processing
Ensemble learning
Unsupervised
Multiclass classification
Crowdsourcing
Tractament del senyal
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
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.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01
2018
2018-08-03
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/120513
https://dx.doi.org/10.1109/TSP.2018.2860562
url https://hdl.handle.net/2117/120513
https://dx.doi.org/10.1109/TSP.2018.2860562
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 TEC2015-69648-REDC RED COMONSENS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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