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dc.contributor.authorCruz Barbosa, Raúl
dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2010-10-07T09:57:36Z
dc.date.available2010-10-07T09:57:36Z
dc.date.created2008-09
dc.date.issued2008-09
dc.identifier.citationCruz, R.; Vellido, A. Unfolding the Manifold in Generative Topographic Mapping. "Lecture notes in computer science", Setembre 2008, vol. 5271, p. 392-399.
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/2117/9511
dc.description.abstractGenerative Topographic Mapping (GTM) is a probabilistic latent variable model for multivariate data clustering and visualization. It tries to capture the relevant data structure by defining a low-dimensional manifold embedded in the high-dimensional data space. This requires the assumption that the data can be faithfully represented by a manifold of much lower dimension than that of the observed space. Even when this assumption holds, the approximation of the data may, for some datasets, require plenty of folding, resulting in an entangled manifold and in breaches of topology preservation that would hamper data visualization and cluster definition. This can be partially avoided by modifying the GTM learning procedure so as to penalize divergences between the Euclidean distances from the data to the model prototypes and the corresponding geodesic distances along the manifold. We define and assess this strategy, comparing it to the performance of the standard GTM, using several artificial datasets.
dc.format.extent8 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subject.lcshGenerative Topographic Mapping (GTM)
dc.titleUnfolding the Manifold in Generative Topographic Mapping
dc.typeArticle
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac1623394
dc.description.versionPostprint (published version)
local.citation.authorCruz, R.; Vellido, A.
local.citation.publicationNameLecture notes in computer science
local.citation.volume5271
local.citation.startingPage392
local.citation.endingPage399


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