Graph-Based Semi-Supervised Learning on Evolutionary Data

This paper presents a graph based semi-supervised learning algorithm on evolutionary data. By applying evolutionary smoothness assumption and incorporating it to the general framework of graph-based semi-supervised learning, we got a new algorithm called GSSLE. Empirical evaluations show that our method outperforms other state-of-the-art methods in terms of stability. It is able to deal with dynamic feature space tasks and proves efficient even if we do not have many unlabeled samples in the semi-supervised procedure.