A passive-aggressive algorithm for semi-supervised learning

In this paper, we proposed a novel semi-supervised learning algorithm, named passive-aggressive semi-supervised learner, which consists of the concepts of passive-aggressive, down-weighting, and multi-view scheme. Our approach performs the labeling and training procedures iteratively. In labeling procedure, we use two views, known as teacher's classifiers for consensus training to obtain a set of guessed labeled points. In training procedure, we use the idea of down-weighting to retrain the third view, i.e., student's classifier by the given initial labeled and guessed labeled points. Based on the idea of passive-aggressive algorithm, we would also like the new retrained classifier to be held as near as possible to the original classifier produced by the initial labeled data. The experiment results showed that our method only uses a small portion of the labeled training data points, but its test accuracy is comparable to the pure supervised learning scheme that uses all the labeled data points for training.