Humans Learn Using Manifolds, Reluctantly

author: Jerry (Xiaojin) Zhu, Department of Computer Sciences, University of Wisconsin-Madison
published: Jan. 12, 2011,   recorded: December 2010,   views: 4845
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Description

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.

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