Graph-based semi-supervised learning

Abstract

The recent years have witnessed a surge of interests in graph-based semi-supervised learning (GBSSL). In this paper, we will introduce a series of works done by our group on this topic including: 1) a method called linear neighborhood propagation (LNP) which can automatically construct the optimal graph; 2) a novel multilevel scheme to make our algorithm scalable for large data sets; 3) a generalized point charge scheme for GBSSL; 4) a multilabel GBSSL method by solving a Sylvester equation; 5) an information fusion framework for GBSSL; and 6) an application of GBSSL on fMRI image segmentation.