Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Journal of Combinatorial Theory Series A
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Block-quantized kernel matrix for fast spectral embedding
ICML '06 Proceedings of the 23rd international conference on Machine learning
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Learning
Towards a theoretical foundation for laplacian-based manifold methods
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Semisupervised Learning Based on Generalized Point Charge Models
IEEE Transactions on Neural Networks
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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.