Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)
Tim Rogers, Chuck Kalish, Joseph Harrison, Jerry Zhu, Bryan Gibson
When the distribution of unlabeled data in feature space lies along a manifold, the information it provides may be used by a learner to assist classification in a semi-supervised setting. While manifold learning is well-known in machine learning, the use of manifolds in human learning is largely unstudied. We perform a set of experiments which test a human's ability to use a manifold in a semi-supervised learning task, under varying conditions. We show that humans may be encouraged into using the manifold, overcoming the strong preference for a simple, axis-parallel linear boundary.