A speed-up algorithm for Poisson Propagation

Based on the theory of electrostatic field, a novel semi-supervised learning method named Poisson propagation has been proposed by Fei Wang. In his formulation, data are regarded as points in the field and the labels of unlabeled data points are propagated from labeled sources, which is like the field responses modeled by Poisson´s equation. In this paper, we develop an efficient way for accelerating the PP algorithm, and also provide the theoretical analysis of the optimality of such acceleration approach. Our method is tested on 6 different data sets. The experiment results show the effectiveness of our acceleration algorithm.