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  Getting lost in space: Large sample analysis of the resistance distance

von Luxburg, U., Radl, A., & Hein, M. (2011). Getting lost in space: Large sample analysis of the resistance distance. In J. Lafferty (Ed.), 24th Annual Conference on Neural Information Processing Systems (NIPS 2010) (pp. 2622-2630). Red Hook, NY: Curran.

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 Creators:
von Luxburg, U.1, Author           
Radl, A.2, Author           
Hein, M.3, Author
Affiliations:
1Research Group Machines Learning Theory, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497665              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
3Max Planck Society, ou_persistent13              

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Free keywords: MPI für Intelligente Systeme; Abt. Schölkopf;
 Abstract: The commute distance between two vertices in a graph is the expected time it takes a random walk to travel from the first to the second vertex and back. We study the behavior of the commute distance as the size of the underlying graph increases. We prove that the commute distance converges to an expression that does not take into account the structure of the graph at all and that is completely meaningless as a distance function on the graph. Consequently, the use of the raw commute distance for machine learning purposes is strongly discouraged for large graphs and in high dimensions. As an alternative we introduce the amplified commute distance that corrects for the undesired large sample effects.

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 Dates: 2011-06-01
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
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 Degree: -

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Title: 24th Annual Conference on Neural Information Processing Systems (NIPS 2010)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2010-12-06 - 2010-12-09

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Title: 24th Annual Conference on Neural Information Processing Systems (NIPS 2010)
Source Genre: Proceedings
 Creator(s):
Lafferty, J., Editor
Affiliations:
-
Publ. Info: Red Hook, NY : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2622 - 2630 Identifier: -

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Title: Advances in neural information processing systems
Source Genre: Series
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Pages: - Volume / Issue: 23 Sequence Number: - Start / End Page: - Identifier: -