Mostra el registre d'ítem simple
A variational Bayesian formulation for GTM: Theoretical foundations
dc.contributor.author | Olier Caparroso, Iván |
dc.contributor.author | Vellido Alcacena, Alfredo |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.date.accessioned | 2016-04-28T08:12:40Z |
dc.date.available | 2016-04-28T08:12:40Z |
dc.date.issued | 2007-09 |
dc.identifier.citation | Olier, I., Vellido, A. "A variational Bayesian formulation for GTM: Theoretical foundations". 2007. |
dc.identifier.uri | http://hdl.handle.net/2117/86314 |
dc.description.abstract | Generative Topographic Mapping (GTM) is a non-linear latent variable model of the manifold learning family that provides simultaneous visualization and clustering of high-dimensional data. It was originally formulated as a constrained mixture of Gaussian distributions, for which the adaptive parameters were determined by Maximum Likelihood (ML), using the Expectation-Maximization (EM) algorithm. In this paper, we define an alternative variational formulation of GTM that provides a full Bayesian treatment to a Gaussian Process (GP) - based variation of the model. |
dc.format.extent | 8 p. |
dc.language.iso | eng |
dc.relation.ispartofseries | LSI-07-33-R |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.other | Generative models |
dc.subject.other | Variational inference |
dc.subject.other | Statistical machine learning |
dc.title | A variational Bayesian formulation for GTM: Theoretical foundations |
dc.type | External research report |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.rights.access | Open Access |
local.identifier.drac | 1841859 |
dc.description.version | Postprint (published version) |
local.citation.author | Olier, I.; Vellido, A. |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Reports de recerca [55]
-
Reports de recerca [1.107]