Effective transductive learning via objective model selection

Authors:
Ran El-Yaniv;Leonid Gerzon
Affiliations:
Computer Science Department, Technion-Israel Institute of Technology, Haifa 32000, Israel;Department of Mathematics, Technion-Israel Institute of Technology, Haifa 32000, Israel
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

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.