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Clustering and visualization of multivariate time series
dc.contributor.author | Vellido Alcacena, Alfredo |
dc.contributor.author | Olier Caparroso, Iván |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.date.accessioned | 2011-07-19T11:03:28Z |
dc.date.available | 2011-07-19T11:03:28Z |
dc.date.created | 2009 |
dc.date.issued | 2009 |
dc.identifier.citation | Vellido, A.; Olier, I. Clustering and visualization of multivariate time series. A: "Handbook of research on machine learning applications and trends: algorithms, methods, and techniques". Information Science Reference, 2009, p. 176-194. |
dc.identifier.isbn | 978-1-60566-766-9 |
dc.identifier.uri | http://hdl.handle.net/2117/13006 |
dc.description.abstract | The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if not impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on the exploration of MTS trough unsupervised clustering and visualization. In this chapter, the authors describe generative topographic mapping through time (GTM-TT), a model with foundations in probability theory that performs such tasks. The standard version of this model has several limitations that limit its applicablility. Here, the authors reformulate it within a Bayesian approach using variational techniques. The resulting variational Bayesian GTM-TT, described in some details, is shown to behave very robustly in the presence of noise in the MTS, helping to avert the poblem of data overfitting. |
dc.format.extent | 19 p. |
dc.language.iso | eng |
dc.publisher | Information Science Reference |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Time-series analysis |
dc.subject.lcsh | Multivariate analysis |
dc.title | Clustering and visualization of multivariate time series |
dc.type | Part of book or chapter of book |
dc.subject.lemac | Sèries temporals -- Anàlisi |
dc.subject.lemac | Anàlisi multivariable |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.infosci-journals.com/reference/details.asp?id=34664 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 3124948 |
dc.description.version | Postprint (published version) |
local.citation.author | Vellido, A.; Olier, I. |
local.citation.publicationName | Handbook of research on machine learning applications and trends: algorithms, methods, and techniques |
local.citation.startingPage | 176 |
local.citation.endingPage | 194 |
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