Eigensolver Methods for Progressive Multidimensional Scaling of Large Data
Brandes, Ulrik and Pich, Christian (2007) Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. In: Graph Drawing 14th International Symposium, GD 2006, September 18-20, 2006, Karlsruhe, Germany , pp. 42-53 (Official URL: http://dx.doi.org/10.1007/978-3-540-70904-6_6).
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We present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs. It produces layouts that compare favorably with other methods for drawing large graphs, and it is among the fastest methods available. In addition, our approach allows for progressive computation, i.e. a rough approximation of the layout can be produced even faster, and then be refined until satisfaction.
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