A survey of large-scale graph-based semi-supervised classification algorithms
Yunsheng Song, Jing Zhang, Chao Zhang
Abstract
Semi-supervised learning is an effective method to study how to use both labeled data and unlabeled data to improve the performance of the classifier, which has become the hot field of machine learning in recent years. Graph-based semi-supervised learning is very promising among these Semi-supervised methods for its good performance and universality, it represents all the data as a graph and the label information is propagated from the labeled data to the unlabeled data along with the constructed graph. To improve the scalability of graph-based semi-supervised methods for large-scale data, an increasing number of methods with granulation mechanisms are proposed. However, there exist few papers to solely analyze the recent research progress. Following the process of graph-based semi-supervised learning, this paper concludes with the fundamental principles of the granulation mechanism for graph construction and label inference respectively. Moreover, a new taxonomy is generated according to the granulation criterion for each process. Therefore, it provides an important reference for the use and research of semi-supervised classification algorithms facing massive data.