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, June 2010, Haifa, Israel.

Abstract

We show that the mistake bound for predicting the nodes of an arbitrary weighted graph is characterized (up to logarithmic factors) by the weighted 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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7510
Deposited By:Claudio Gentile
Deposited On:17 March 2011