关键词: 半监督学习, 标签传播, 弹力理论, 收敛

Abstract:

In essence, existing graph based semi-supervised learning methods belong to label propagation ones by simulating various propagation mechanics. In this study, different from the existing propagation mechanics, it attempts to exploit a novel elastic-force based propagation method to realize semi-supervised learning. The basic idea is to imagine that each node in a graph accepts elastic forces in an elastic coefficient from its neighbors and transmits elastic forces to its neighbors in another elastic coefficient. As a result, the difference between two types of elastic forces measures the propagation quantity of each node. Based on this novel idea, this paper derives the corresponding update equations of all nodes in the graph, which will further induce an analytical solution by expressing these equations in a matrix. In other words, the proposed method has its reliable foundation from the philosophy of physics. Besides, it also demonstrates the rationale of the proposed method from the perspective of optimizing the corresponding objective function, which guarantees the convergence of the proposed method. The extensive experimental results verify the effectiveness of the proposed method in semi-supervised learning.

Key words: semi-supervised learning, label propagation, elastic-force, convergence