PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Getting lost in space: Large sample analysis of the commute distance
Ulrike v. Luxburg, Agnes Radl and Matthias Hein
In: NIPS 2010(2010).

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7278
Deposited By:Ulrike Von Luxburg
Deposited On:16 March 2011