PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

See the tree through the lines: the Shazoo algorithm
Fabio Vitale, Nicolò Cesa-Bianchi, Claudio Gentile and Giovanni Zappella
In: Nips 2011, Dec 12th - Dec 17th, 2011, Granada, Spain.

Abstract

Predicting the nodes of a given graph is a fascinating theoretical problem with applications in several domains. Since graph sparsification via spanning trees retains enough information while making the task much easier, trees are an important special case of this problem. Although it is known how to predict the nodes of an unweighted tree in a nearly optimal way, in the weighted case a fully satisfactory algorithm is not available yet. We fill this hole and introduce an efficient node predictor, SHAZOO, which is nearly optimal on any weighted tree. Moreover, we show that SHAZOO can be viewed as a common nontrivial generalization of both previous approaches for unweighted trees and weighted lines. Experiments on real-world datasets confirm that SHAZOO performs well in that it fully exploits the structure of the input tree, and gets very close to (and sometimes better than) less scalable energy minimization methods.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:8973
Deposited By:Claudio Gentile
Deposited On:21 February 2012