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dc.contributor.authorOlier Caparroso, Iván
dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2016-04-28T09:20:38Z
dc.date.available2016-04-28T09:20:38Z
dc.date.issued2007-10
dc.identifier.citationOlier, I., Vellido, A. "A variational formulation for GTM through time: Theoretical foundations". 2007.
dc.identifier.urihttp://hdl.handle.net/2117/86323
dc.description.abstractGenerative Topographic Mapping (GTM) is a latent variable model that, in its standard version, was conceived to provide clustering and visualization of multivariate, real-valued, i.i.d. data. It was also extended to deal with non-i.i.d. data such as multivariate time series in a variant called GTM Through Time (GTMTT), defined as a constrained Hidden Markov Model (HMM). In this technical report, we provide the theoretical foundations of the reformulation of GTM-TT within the Variational Bayesian framework. This approach, in its application, should naturally handle the presence of noise in the time series, helping to avert the problem of data overfitting.
dc.format.extent10 p.
dc.language.isoeng
dc.relation.ispartofseriesLSI-07-38-R
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.otherVariational
dc.subject.otherGTM through time
dc.subject.otherMultivariate time series
dc.titleA variational formulation for GTM through time: Theoretical foundations
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.rights.accessOpen Access
local.identifier.drac1841864
dc.description.versionPostprint (published version)
local.citation.authorOlier, I.; Vellido, A.


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