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Margin based transductive graph cuts using linear programming

Pelckmans, K; Suykens, JAK; De Moor, B; Shawe-Taylor, J; (2007) Margin based transductive graph cuts using linear programming. In: Journal of Machine Learning Research. (pp. 363 - 370). Gold open access

Abstract

This paper studies the problem of inferring a partition (or a graph cut) of an undirected deterministic graph where the labels of some nodes are observed thereby bridging a gap between graph theory and probabilistic inference techniques. Given a weighted graph, we focus on the rules of weighted neighbors to predict the label of a particular node. A maximum margin and maximal average margin based argument is used to prove a generalization bound, and is subsequently related to the classical MINCUT approach. From a practical perspective a simple and intuitive, but efficient convex formulation is constructed. This scheme can readily be implemented as a linear program which scales well till a few thousands of (labeled or unlabeled) data-points. The extremal case is studied where one observes only a single label, and this setting is related to the task of unsupervised clustering.

Type: Proceedings paper
Title: Margin based transductive graph cuts using linear programming
Open access status: An open access publication
Publisher version: http://www.jmlr.org/
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/79216
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