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2010 IEEE International Conference on Data Mining Workshops (2010)
Sydney, Australia
Dec. 13, 2010 to Dec. 13, 2010
ISBN: 978-0-7695-4257-7
pp: 1220-1227
ABSTRACT
We describe simple yet scalable and distributed algorithms for solving the maximum flow problem and its minimum cost flow variant, motivated by problems of interest in objects similarity visualization. We formulate the fundamental problem as a convex-concave saddle point problem. We then show that this problem can be efficiently solved by a first order method or by exploiting faster quasi-Newton steps. Our proposed approach costs at most O(|E|) per iteration for a graph with |E| edges. Further, the number of required iterations can be shown to be independent of number of edges for the first order approximation method. We present experimental results in two applications: mosaic generation and color similarity based image layouting.
INDEX TERMS
Visualization, Flow networks, Distributed algorithms, Linear programming
CITATION

A. J. Smola, N. Quadrianto and D. Schuurmans, "Distributed Flow Algorithms for Scalable Similarity Visualization," 2010 IEEE International Conference on Data Mining Workshops(ICDMW), Sydney, Australia, 2010, pp. 1220-1227.
doi:10.1109/ICDMW.2010.120
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