Manifold regularization and semi-supervised learning: some theoretical analyses

  title={Manifold regularization and semi-supervised learning: some theoretical analyses},
  author={Partha Niyogi},
  journal={Journal of Machine Learning Research},
  • Partha Niyogi
  • Published 2013 in Journal of Machine Learning Research
Manifold regularization (Belkin et al., 2006) is a geometri cally motivated framework for machine learning within which several semi-supervised algorithms have been constructed. Here we try to provide some theoretical understanding of this approach. O ur main result is to expose the natural structure of a class of problems on which manifold regulariz ation methods are helpful. We show that for such problems, no supervised learner can learn effe ctively. On the other hand, a manifold based… CONTINUE READING
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