Enhancing label inference algorithms considering vertex importance in graph-based semi-supervised learning

摘要

Graph-based semi-supervised learning has recently come into focus for to its two defining phases: graph construction, which converts the data into a graph, and label inference, which predicts the appropriate labels for unlabeled data using the constructed graph. And the label inference is based on the smoothness assumption of semi-supervised learning. In this study, we propose an enhanced label inference approach which incorporates the importance of each vertex into the existing inference algorithms to improve the prediction capabilities of the algorithms. We also present extensions of three algorithms which are capable of taking the vertex importance variable to apply in learning. Experiments show that our algorithms perform better than the base algorithms on a variety of datasets, especially when the data is less smooth over the graphs.