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Probability ridges and distortion flows: Visualizing multivariate time series using a variational Bayesian manifold learning method
dc.contributor.author | Tosi, Alessandra |
dc.contributor.author | Olier, Iván |
dc.contributor.author | Vellido Alcacena, Alfredo |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2016-02-15T15:20:06Z |
dc.date.available | 2016-02-15T15:20:06Z |
dc.date.issued | 2014 |
dc.identifier.citation | Tosi, 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.isbn | 978-3-319-07695-9 |
dc.identifier.uri | http://hdl.handle.net/2117/82956 |
dc.description.abstract | Time-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.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.lcsh | Information visualization |
dc.subject.lcsh | Bayesian statistical decision theory |
dc.subject.other | Multivariate time series |
dc.subject.other | Nonlinear dimensionality reduction |
dc.subject.other | Mapping distortion |
dc.subject.other | Magnification factors |
dc.subject.other | Visualization |
dc.subject.other | Generative topographic mapping |
dc.subject.other | Variational Bayesian methods |
dc.title | Probability ridges and distortion flows: Visualizing multivariate time series using a variational Bayesian manifold learning method |
dc.type | Conference report |
dc.subject.lemac | Visualització de la informació |
dc.subject.lemac | Estadística bayesiana |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.identifier.doi | 10.1007/978-3-319-07695-9_5 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://link.springer.com/chapter/10.1007%2F978-3-319-07695-9_5 |
dc.rights.access | Open Access |
local.identifier.drac | 17499600 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Tosi, A.; Olier, I.; Vellido, A. |
local.citation.contributor | Workshop on Self-Organizing Maps |
local.citation.pubplace | Mittweida |
local.citation.publicationName | 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) |
local.citation.startingPage | 55 |
local.citation.endingPage | 64 |