Optimization Approaches for Large-Scale Semi-Supervised Learning

Abstract

We present optimization approaches for semi-supervised learning for classification based on the formulations of Support Vector Machine (SVM) for the conventional supervised setting. We first introduce the Laplacian of a graph and the associated graph kernels which are exploited in many semi-supervised classification methods. We will show that these methods can be naturally derived from the conventional formulations of SVMs with the graph kernels. 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 numerical results indicate that our approaches achieve fairly high performance on large scale problems.