Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions.
Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stagewise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real-world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms, including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.
Chen
Ke
K
School of Computer Science, The University of Manchester, Kilburn Building, Oxford Road, Manchester M13 9PL, UK. chen@cs.manchester.ac.uk
Wang
Shihai
S
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D007858:Learning; D010363:Pattern Recognition, Automated / Q000379:methods