Generalization performance of graph-based semi-supervised classification

Semi-supervised learning has been of growing interest over the past few years and many methods have been proposed. Although various algorithms are provided to implement semi-supervised learning, there are still gaps in our understanding of the dependence of generalization error on the numbers of labeled and unlabeled data. In this paper, we consider a graph-based semi-supervised classification algorithm and establish its generalization error bounds. Our results show the close relations between the generalization performance and the structural invariants of data graph.