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Multi-Manifold Semi-Supervised Learning

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journal contribution
posted on 01.04.2009, 00:00 by Andrew B. Goldberg, Xiaojin Zhu, Aarti Singh, Zhiting Xu, Robert Nowak

We study semi-supervised learning when the data consists of multiple intersecting manifolds. We give a finite sample analysis to quantify the potential gain of using unlabeled data in this multi-manifold setting. We then propose a semi-supervised learning algorithm that separates different manifolds into decision sets, and performs supervised learning within each set. Our algorithm involves a novel application of Hellinger distance and size-constrained spectral clustering. Experiments demonstrate the benefit of our multi-manifold semi-supervised learning approach

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Copyright 2009 by the authors

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01/04/2009

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