Semi-Supervised Classification by Low Density Separation (2005)
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BibTeX
@MISC{Chapelle05semi-supervisedclassification,
author = {Olivier Chapelle and Alexander Zien},
title = {Semi-Supervised Classification by Low Density Separation},
year = {2005}
}
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Abstract
We believe that the cluster assumption is key to successful semi-supervised learning. Based on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances that emphazise low density regions between clusters, followed by training a standard SVM; 2. optimizing the Transductive SVM objective function, which places the decision boundary in low density regions, by gradient descent; 3. combining the first two to make maximum use of the cluster assumption. We compare with state of the art algorithms and demonstrate superior accuracy for the latter two methods.
