Effective transductive learning via objective model selection

This paper is concerned with transductive learning. We study a recent transductive learning approach based on clustering. In this approach one constructs a diversity of unsupervised models of the unlabeled data using clustering algorithms. These models are then exploited to construct a number of hypotheses using the labeled data and the learner selects an hypothesis that minimizes a transductive error bound, which holds with high probability. Empirical examination of this approach, implemented with ‘spectral clustering’, on a suite of benchmark datasets from the UCI repository, indicates that the new approach is effective and comparable with one of the best known transductive learning algorithms to-date.