Semi-Supervised Learning with Adaptive Spectral Transform
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:902-910, 2016.
This paper proposes a novel nonparametric framework for semi-supervised learning and for optimizing the Laplacian spectrum of the data manifold simultaneously. Our formulation leads to a convex optimization problem that can be efficiently solved via the bundle method, and can be interpreted as to asymptotically minimize the generalization error bound of semi-supervised learning with respect to the graph spectrum. Experiments over benchmark datasets in various domains show advantageous performance of the proposed method over strong baselines.