Optimization approaches for semi-supervised learning

We present new approaches for semi-supervised learning based on the formulations of SVMs for the conventional supervised setting. The manifold structure of the data points given by the graph Laplacian can be taken into account in a efficient way. The proposed optimization problems fully enjoy the sparse structure of the graph Laplacian, which enables us to optimize the problems with a large number of data points in a practical amount of computational time. Some results of experiments showing the performance of our approaches are presented.