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 boost...

• Type: Journal
• Year: 2011
• Where: PAMI
• Authors: Ke Chen, Shihai Wang
• Keywords: Boosting, Operations Research, PAMI 2011, Semi-supervised Assumptions, Semi-supervised Learning