Conference article

Glint: An MDS Framework for Costly Distance Functions

Stephen Ingram
University of British Columbia, Canada

Tamara Munzner
University of British Columbia, Canada

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Published in: Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden

Linköping Electronic Conference Proceedings 81:5, p. 29-38

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Published: 2012-11-20

ISBN: 978-91-7519-723-4

ISSN: 1650-3686 (print), 1650-3740 (online)


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.


I.3.3 [Human-centered Computing]: Visualization—Visualization systems and tools


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