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dc.contributor.authorTino, P.
dc.contributor.authorNabney, I.
dc.contributor.authorSun, Yi.
dc.contributor.authorWilliams, B.S.
dc.date.accessioned2016-03-08T15:38:25Z
dc.date.available2016-03-08T15:38:25Z
dc.date.issued2001
dc.identifier.citationTino , P , Nabney , I , Sun , Y & Williams , B S 2001 , A principled approach to interactive hierarchical non-linear visualization of high-dimensional data . in Interface '01 - Frontiers in Data Mining and Bioinformatics .
dc.identifier.otherPURE: 407216
dc.identifier.otherPURE UUID: 4d66b156-379b-464c-8ed0-62f1e7f99854
dc.identifier.otherdspace: 2299/836
dc.identifier.urihttp://hdl.handle.net/2299/16757
dc.description.abstractHierarchical visualization systems are desirable because a single twodimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex high-dimensional data sets. We extend an existing locally linear hierarchical visualization system PhiVis [1] in several directions: (1) we allow for non-linear projection manifolds (the basic building block is the Generative Topographic Mapping – GTM), (2) we introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree, (3) we describe folding patterns of low-dimensional projection manifold in high-dimensional data space by computing and visualizing the manifold’s local directional curvatures. Quantities such as magnification factors [3] and directional curvatures are helpful for understanding the layout of the nonlinear projection manifold in the data space and for further refinement of the hierarchical visualization plot. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. We demonstrate the visualization system principle of the approach on a complex 12-dimensional data set and mention possible applications in the pharmaceutical industry.en
dc.language.isoeng
dc.relation.ispartofInterface '01 - Frontiers in Data Mining and Bioinformatics
dc.rightsOpen
dc.titleA principled approach to interactive hierarchical non-linear visualization of high-dimensional dataen
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.relation.schoolSchool of Physics, Engineering & Computer Science
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2001
rioxxterms.versionAM
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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