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Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments
dc.contributor.author | Vellido Alcacena, Alfredo |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2016-12-09T08:46:55Z |
dc.date.available | 2016-12-09T08:46:55Z |
dc.date.issued | 2004-09 |
dc.identifier.citation | Vellido, A. "Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments". 2004. |
dc.identifier.uri | http://hdl.handle.net/2117/97911 |
dc.description.abstract | 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. |
dc.format.extent | 12 p. |
dc.language.iso | eng |
dc.relation.ispartofseries | LSI-04-44 |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.other | Generative topographic mapping |
dc.subject.other | GTM |
dc.subject.other | Gaussian mixture models |
dc.subject.other | Outliers |
dc.subject.other | Student t-distributions |
dc.title | Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments |
dc.type | External research report |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.rights.access | Open Access |
local.identifier.drac | 1841825 |
dc.description.version | Postprint (published version) |
local.citation.author | Vellido, A. |
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