De Moor, B;
Margin based transductive graph cuts using linear programming.
Presented at: UNSPECIFIED.
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:||Conference item (UNSPECIFIED)|
|Title:||Margin based transductive graph cuts using linear programming|
|Open access status:||An open access publication|
|Keywords:||Clustering, Combinatorial and convex optimization, Graph cuts, Statistical learning, Transductive inference|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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