Semi-supervised learning with max-margin graph cuts
The algorithm learns graph cuts that maximize a margin based on labels derived from harmonic functions on a data graph. This convex approach combines solving for harmonic functions with a max-margin discriminator to improve semi-supervised learning performance over existing methods like manifold regularization of SVMs.