A volume-based heat-diffusion classifier
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A speed-up algorithm for Poisson propagation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A regularization framework in polar coordinates for transductive learning in networked data
Information Sciences: an International Journal
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The recent years have witnessed a surge of interest in semisupervised learning. Numerous methods have been proposed for learning from partially labeled data. In this brief, a novel semisupervised learning approach based on an electrostatic field model is proposed. We treat the labeled data points as point charges, therefore the remaining unlabeled data points are placed in the electrostatic fields generated by these charges. The labels of these unlabeled data points can be regarded as the electric potentials of the electrostatic field at their corresponding places. Moreover, we also develop an efficient way to extend our method for out-of-sample data and analyze theoretically the relationship between our method and the traditional graph-based methods. Finally, the experimental results on both toy and real-world data sets are provided to show the effectiveness of our method.