Graph-based semi-supervised learning: A review

Considering the labeled samples may be difficult to obtain because they require human annotators, special devices, or expensive and slow experiments. Semi-supervised learning (SSL) has tremendous practical value. Moreover, graph-based SSL methods have received more attention since their convexity, scalability and effectiveness in practice. The convexity of graph-based SSL guarantees that the optimization problems become easier to obtain local solution than the general case. The scalable graph-based SSL methods are convenient to deal with large-scale dataset for big data. Graph-based SSL methods aim to learn the predicted function for the labels of those unlabeled samples by exploiting the label dependency information reflected by available label information. The main purpose of this paper is to provide a comprehensive study of graph-based SSL. Specifically, the concept of the graph is first given before introducing graph-based semi-supervised learning. Then, we build a framework that divides the corresponding works into transductive graph-based SSL, inductive graph-based SSL, and scalable graph-based SSL. The core idea of these models is to impose graph constraints to the optimal function, which guarantees the smoothness over the graph. Next, several representative graph-based SSL methods are conducted on the three data sets, including two face data sets and a natural image data set. Finally, we outlook several directions for future work of graph-based SSL, and hope our review on graph-based SSL will offer insights for further research. (c) 2020 Elsevier B.V. All rights reserved.