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dc.contributor.authorBlanco Valencia, Xiomara Patricia
dc.contributor.authorBecerra, M. A.
dc.contributor.authorCastro Ospina, A. E.
dc.contributor.authorOrtega Adarme, M.
dc.contributor.authorViveros Melo, D.
dc.contributor.authorPeluffo Ordóñez, D. H.
dc.date.accessioned2017-07-26T11:08:06Z
dc.date.available2017-07-26T11:08:06Z
dc.date.issued2017-01-12
dc.identifier.citationADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 6 (2017)
dc.identifier.issn2255-2863
dc.identifier.urihttp://hdl.handle.net/10366/133635
dc.description.abstractThis work outlines a unified formulation to represent spectral approaches for both dimensionality reduction and clustering. Proposed formulation starts with a generic latent variable model in terms of the projected input data matrix.Particularly, such a projection maps data onto a unknown high-dimensional space. Regarding this model, a generalized optimization problem is stated using quadratic formulations and a least-squares support vector machine.The solution of the optimization is addressed through a primal-dual scheme.Once latent variables and parameters are determined, the resultant model outputs a versatile projected matrix able to represent data in a low-dimensional space, as well as to provide information about clusters. Particularly, proposedformulation yields solutions for kernel spectral clustering and weighted-kernel principal component analysis.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherEdiciones Universidad de Salamanca (España)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputación
dc.subjectInformótica
dc.subjectComputing
dc.subjectInformation Technology
dc.titleKernel-based framework for spectral dimensionality reduction and clustering formulation: A theoretical study
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported