Margin based Transductive Graph Cuts using Linear Programming
K. Pelckmans, J. Shawe-Taylor, J.A.K. Suykens, B. De Moor;
JMLR W&P 2:363-370, 2007.
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.