Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments

The Generative Topographic Mapping (GTM: Bishop et al. 1998a), a non-linear latent variable model, was originally defined as constrained mixture of Gaussians. Gaussian mixture models are known to lack robustness in the presence of outlier observations in the data sample, and multivariate Student t-d...

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Detalles Bibliográficos
Autor: Vellido Alcacena, Alfredo|||0000-0002-9843-1911
Tipo de recurso: informe técnico
Fecha de publicación:2004
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/97911
Acceso en línea:https://hdl.handle.net/2117/97911
Access Level:acceso abierto
Palabra clave:Generative topographic mapping
GTM
Gaussian mixture models
Outliers
Student t-distributions
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
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spelling Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developmentsVellido Alcacena, Alfredo|||0000-0002-9843-1911Generative topographic mappingGTMGaussian mixture modelsOutliersStudent t-distributionsÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialThe Generative Topographic Mapping (GTM: Bishop et al. 1998a), a non-linear latent variable model, was originally defined as constrained mixture of Gaussians. Gaussian mixture models are known to lack robustness in the presence of outlier observations in the data sample, and multivariate Student t-distributions have recently been put forward as a more robust alternative to deal with continuous data in this context.20042004-09-0120162016-12-09reporthttp://purl.org/coar/resource_type/c_93fcVoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/reportapplication/postscripthttps://hdl.handle.net/2117/97911reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/979112026-05-27T15:37:01Z
dc.title.none.fl_str_mv Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
title Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
spellingShingle Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
Vellido Alcacena, Alfredo|||0000-0002-9843-1911
Generative topographic mapping
GTM
Gaussian mixture models
Outliers
Student t-distributions
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
title_full Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
title_fullStr Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
title_full_unstemmed Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
title_sort Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
dc.creator.none.fl_str_mv Vellido Alcacena, Alfredo|||0000-0002-9843-1911
author Vellido Alcacena, Alfredo|||0000-0002-9843-1911
author_facet Vellido Alcacena, Alfredo|||0000-0002-9843-1911
author_role author
dc.subject.none.fl_str_mv Generative topographic mapping
GTM
Gaussian mixture models
Outliers
Student t-distributions
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Generative topographic mapping
GTM
Gaussian mixture models
Outliers
Student t-distributions
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description The Generative Topographic Mapping (GTM: Bishop et al. 1998a), a non-linear latent variable model, was originally defined as constrained mixture of Gaussians. Gaussian mixture models are known to lack robustness in the presence of outlier observations in the data sample, and multivariate Student t-distributions have recently been put forward as a more robust alternative to deal with continuous data in this context.
publishDate 2004
dc.date.none.fl_str_mv 2004
2004-09-01
2016
2016-12-09
dc.type.none.fl_str_mv report
http://purl.org/coar/resource_type/c_93fc
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/97911
url https://hdl.handle.net/2117/97911
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
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/postscript
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
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repository.mail.fl_str_mv
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