Visualizing And Interacting With Kernelized Data

dc.creatorBarbosa
dc.creatorA.; Paulovich
dc.creatorF. V.; Paiva
dc.creatorA.; Goldenstein
dc.creatorS.; Petronetto
dc.creatorF.; Nonato
dc.creatorL. G.
dc.date2016
dc.datemar
dc.date2017-11-13T13:14:45Z
dc.date2017-11-13T13:14:45Z
dc.date.accessioned2018-03-29T05:52:28Z
dc.date.available2018-03-29T05:52:28Z
dc.identifierIeee Transactions On Visualization And Computer Graphics. Ieee Computer Soc, v. 22, p. 1314 - 1325, 2016.
dc.identifier1077-2626
dc.identifier1941-0506
dc.identifierWOS:000370435700012
dc.identifier10.1109/TVCG.2015.2464797
dc.identifierhttp://ieeexplore.ieee.org.ez88.periodicos.capes.gov.br/document/7180398/
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/327235
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1364260
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionKernel-based methods have experienced a substantial progress in the last years, tuning out an essential mechanism for data classification, clustering and pattern recognition. The effectiveness of kernel-based techniques, though, depends largely on the capability of the underlying kernel to properly embed data in the feature space associated to the kernel. However, visualizing how a kernel embeds the data in a feature space is not so straightforward, as the embedding map and the feature space are implicitly defined by the kernel. In this work, we present a novel technique to visualize the action of a kernel, that is, how the kernel embeds data into a high-dimensional feature space. The proposed methodology relies on a solid mathematical formulation to map kernelized data onto a visual space. Our approach is faster and more accurate than most existing methods while still allowing interactive manipulation of the projection layout, a game-changing trait that other kernel-based projection techniques do not have.
dc.description22
dc.description3
dc.description1314
dc.description1325
dc.descriptionFAPESP [2011/22749-8, 2013/19760-5, 2014/09546-9]
dc.descriptionCNPq [302643/2013-3]
dc.descriptionCAPES
dc.descriptionSamsung
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.languageEnglish
dc.publisherIEEE Computer Soc
dc.publisherLos Alamitos
dc.relationIEEE Transactions on Visualization and Computer Graphics
dc.rightsfechado
dc.sourceWOS
dc.subjectMultidimensional Projection
dc.subjectVisualization
dc.subjectKernel Methods
dc.titleVisualizing And Interacting With Kernelized Data
dc.typeArtículos de revistas


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