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Authors:Stephen Ingram: University of British Columbia, Canada
Tamara Munzner: University of British Columbia, Canada
Publication title:Glint: An MDS Framework for Costly Distance Functions
Conference:Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29–30; 2012; Växjö; Sweden
Publication type: Abstract and Fulltext
Issue:081
Article No.:005
Abstract:Previous algorithms for multidimensional scaling; or MDS; aim for scalable performance as the number of points to lay out increases. However; they either assume that the distance function is cheap to compute; and perform poorly when the distance function is costly; or they leave the precise number of distances to compute as a manual tuning parameter. We present Glint; an MDS algorithm framework that addresses both of these shortcomings. Glint is designed to automatically minimize the total number of distances computed by progressively computing a more and more densely sampled approximation of the distance matrix. We present instantiations of the Glint framework on three different classes of MDS algorithms: force-directed; analytic; and gradient-based. We validate the framework through computational benchmarks on several real-world datasets; and demonstrate substantial performance benefits without sacrificing layout quality.
Language:English
Keywords:I.3.3 [Human-centered Computing]: Visualization—Visualization systems and tools
Year:2012
No. of pages:10
Pages:29-38
ISBN:978-91-7519-723-4
Series:Linköping Electronic Conference Proceedings
ISSN (print):1650-3686
ISSN (online):1650-3740
File:http://www.ep.liu.se/ecp/081/005/ecp12081005.pdf
Available:2012-11-20
Publisher:Linköping University Electronic Press; Linköpings universitet

REFERENCE TO THIS PAGE
Stephen Ingram, Tamara Munzner (2012). Glint: An MDS Framework for Costly Distance Functions, Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29–30; 2012; Växjö; Sweden http://www.ep.liu.se/ecp_article/index.en.aspx?issue=081;article=005 (accessed 10/1/2014)