Attribute-based Visual Explanation of Multidimensional Projections

dc.contributor.authorSilva, Renato R. O. daen_US
dc.contributor.authorRauber, Paulo E.en_US
dc.contributor.authorMartins, Rafael M.en_US
dc.contributor.authorMinghim, Rosaneen_US
dc.contributor.authorTelea, Alexandru C.en_US
dc.contributor.editorE. Bertini and J. C. Robertsen_US
dc.date.accessioned2015-05-24T19:45:49Z
dc.date.available2015-05-24T19:45:49Z
dc.date.issued2015en_US
dc.description.abstractMultidimensional projections (MPs) are key tools for the analysis of multidimensional data. MPs reduce data dimensionality while keeping the original distance structure in the low-dimensional output space, typically shown by a 2D scatterplot. While MP techniques grow more precise and scalable, they still do not show how the original dimensions (attributes) influence the projection's layout. In other words, MPs show which points are similar, but not why. We propose a visual approach to describe which dimensions contribute mostly to similarity relationships over the projection, thus explain the projection's layout. For this, we rank dimensions by increasing variance over each point-neighborhood, and propose a visual encoding to show the least-varying dimensions over each neighborhood. We demonstrate our technique with both synthetic and real-world datasets.en_US
dc.description.sectionheadersHigh-dimensional Data and the Design Processen_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)en_US
dc.identifier.doi10.2312/eurova.20151100en_US
dc.identifier.pages31-35en_US
dc.identifier.urihttps://doi.org/10.2312/eurova.20151100en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectH.5.2 [Information Interfaces and Presentation]en_US
dc.subjectUser Interfacesen_US
dc.titleAttribute-based Visual Explanation of Multidimensional Projectionsen_US
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