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dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-12-09T08:46:55Z
dc.date.available2016-12-09T08:46:55Z
dc.date.issued2004-09
dc.identifier.citationVellido, A. "Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments". 2004.
dc.identifier.urihttp://hdl.handle.net/2117/97911
dc.description.abstractThe 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.
dc.format.extent12 p.
dc.language.isoeng
dc.relation.ispartofseriesLSI-04-44
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.otherGenerative topographic mapping
dc.subject.otherGTM
dc.subject.otherGaussian mixture models
dc.subject.otherOutliers
dc.subject.otherStudent t-distributions
dc.titleGenerative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.rights.accessOpen Access
local.identifier.drac1841825
dc.description.versionPostprint (published version)
local.citation.authorVellido, A.


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