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Semi-Supervised Learning with Max-Margin Graph Cuts

Branislav Kveton 1 Michal Valko 2 Ali Rahimi 3 Ling Huang 4
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. We motivate the approach, compare it to existing work, and prove a bound on its generalization error. The quality of our solutions is evaluated on a synthetic problem and three UCI ML repository datasets. In most cases, we outperform manifold regularization of support vector machines, which is a state-of-the-art approach to semi-supervised max-margin learning.
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Submitted on : Saturday, November 19, 2011 - 3:08:19 PM
Last modification on : Saturday, December 18, 2021 - 3:02:33 AM
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Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang. Semi-Supervised Learning with Max-Margin Graph Cuts. International Conference on Artificial Intelligence and Statistics, May 2010, Chia Laguna, Sardinia, Italy. ⟨hal-00642891⟩



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