Concurrent Viewing of Multiple Attribute-Specific Subspaces

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dc.contributor.authorSisneros, Roberten_US
dc.contributor.authorJohnson, C. Ryanen_US
dc.contributor.authorHuang, Jianen_US
dc.contributor.editorA. Vilanova, A. Telea, G. Scheuermann, and T. Moelleren_US
dc.date.accessioned2014-02-21T18:44:58Z
dc.date.available2014-02-21T18:44:58Z
dc.date.issued2008en_US
dc.description.abstractIn this work we present a point classification algorithm for multi-variate data. Our method is based on the concept of attribute subspaces, which are derived from a set of user specified attribute target values. Our classification approach enables users to visually distinguish regions of saliency through concurrent viewing of these subspaces in single images. We also allow a user to threshold the data according to a specified distance from attribute target values. Based on the degree of thresholding, the remaining data points are assigned radii of influence that are used for the final coloring. This limits the view to only those points that are most relevant, while maintaining a similar visual context.en_US
dc.description.number3en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume27en_US
dc.identifier.doi10.1111/j.1467-8659.2008.01208.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2008.01208.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleConcurrent Viewing of Multiple Attribute-Specific Subspacesen_US
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27-Issue 3
EuroVis08: Joint Eurographics - IEEE VGTC Symposium on Visualization