Linear Manifold Regularization for Large Scale Semi-supervised Learning
The enormous wealth of unlabeled data in many applications of machine learning is beginning to pose challenges to the designers of semi-supervised learning methods. We are interested in developing linear classification algorithms to efficiently learn from massive partially labeled datasets. In this paper, we propose Linear Laplacian Support Vector Machines and Linear Laplacian Regularized Least Squares as promising solutions to this problem.