An Iterative Algorithm for Extending Learners to a Semi-Supervised Setting

In this article, we present an iterative self-training algorithm whose objective is to extend learners from a supervised setting into a semi-supervised setting. The algorithm is based on using the predicted values for observations where the response is missing (unlabeled data) and then incorporating the predictions appropriately at subsequent stages. Convergence properties of the algorithm are investigated for particular learners, such as linear/logistic regression and linear smoothers with particular emphasis on kernel smoothers. Further, implementation issues of the algorithm with other learners such as generalized additive models, tree partitioning methods, partial least squares, etc. are also addressed. The connection between the proposed algorithm and graph-based semi-supervised learning methods is also discussed. The algorithm is illustrated on a number of real datasets using a varying degree of labeled responses.