PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Random spanning trees and the prediction of weighted graphs
Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale and Giovanni Zappella
In: ICML 2010, Haifa, Israel(2010).


We show that the mistake bound for predicting the nodes of an arbitrary weighted graph is characterized (up to logarithmic factors) by the cutsize of a random spanning tree of the graph. The cutsize is induced by the unknown adversarial labeling of the graph nodes. In deriving our characterization, we obtain a simple randomized algorithm achieving the optimal mistake bound on any weighted graph. Our algorithm draws a random spanning tree of the original graph and then predicts the nodes of this tree in constant amortized time and linear space. Experiments on real-world datasets show that our method compares well to both global (Perceptron) and local (label-propagation) methods, while being much faster.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7738
Deposited By:Nicolò Cesa-Bianchi
Deposited On:17 March 2011