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dc.contributor.authorTosi, Alessandra
dc.contributor.authorOlier, Iván
dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-02-15T15:20:06Z
dc.date.available2016-02-15T15:20:06Z
dc.date.issued2014
dc.identifier.citationTosi, A., Olier, I., Vellido, A. Probability ridges and distortion flows: Visualizing multivariate time series using a variational Bayesian manifold learning method. A: Workshop on Self-Organizing Maps. "Advances in self-organizing maps and learning vector quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014 (Advances in Intelligent Systems and Computing; 295)". Mittweida: Springer, 2014, p. 55-64.
dc.identifier.isbn978-3-319-07695-9
dc.identifier.urihttp://hdl.handle.net/2117/82956
dc.description.abstractTime-dependent natural phenomena and artificial processes can often be quantitatively expressed as multivariate time series (MTS). As in any other process of knowledge extraction from data, the analyst can benefit from the exploration of the characteristics of MTS through data visualization. This visualization often becomes difficult to interpret when MTS are modelled using nonlinear techniques. Despite their flexibility, nonlinear models can be rendered useless if such interpretability is lacking. In this brief paper, we model MTS using Variational Bayesian Generative Topographic Mapping Through Time (VB-GTM-TT), a variational Bayesian variant of a constrained hidden Markov model of the manifold learning family defined for MTS visualization. We aim to increase its interpretability by taking advantage of two results of the probabilistic definition of the model: the explicit estimation of probabilities of transition between states described in the visualization space and the quantification of the nonlinear mapping distortion.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshInformation visualization
dc.subject.lcshBayesian statistical decision theory
dc.subject.otherMultivariate time series
dc.subject.otherNonlinear dimensionality reduction
dc.subject.otherMapping distortion
dc.subject.otherMagnification factors
dc.subject.otherVisualization
dc.subject.otherGenerative topographic mapping
dc.subject.otherVariational Bayesian methods
dc.titleProbability ridges and distortion flows: Visualizing multivariate time series using a variational Bayesian manifold learning method
dc.typeConference report
dc.subject.lemacVisualització de la informació
dc.subject.lemacEstadística bayesiana
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.identifier.doi10.1007/978-3-319-07695-9_5
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007%2F978-3-319-07695-9_5
dc.rights.accessOpen Access
drac.iddocument17499600
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorTosi, A., Olier, I., Vellido, A.
upcommons.citation.contributorWorkshop on Self-Organizing Maps
upcommons.citation.pubplaceMittweida
upcommons.citation.publishedtrue
upcommons.citation.publicationNameAdvances in self-organizing maps and learning vector quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014 (Advances in Intelligent Systems and Computing; 295)
upcommons.citation.startingPage55
upcommons.citation.endingPage64


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