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
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Using the labeled and unlabeled data to enhance the performance of classification is the core idea of transductive learning, It has recently attracted much interest of researchers on this topic. In this paper, we extend the harmonic energy minimization algorithm and propose a novel transductive learning algorithm on graph with soft label and soft constraint. Relaxing the label to real value makes the transductive problem easy to solve, while softening the hard constraint for the labeled data makes it tolerable to the noise in labeling. We discuss two cases for our algorithm and derive exactly the same form of solution. More importantly, such form of solution can be interpreted from the view of label propagation and a special random walks on graph, which make the algorithm intuitively reasonable. We also discuss several related issues of the proposed algorithm. Experiments on toy examples and real world classification problems demonstrate the effectiveness of our algorithm.