Learning with Local and Global Consistency

  • Denny Zhou
  • Olivier Bousquet
  • Thomas Navin Lal
  • Jason Weston
  • Bernhard Schölkopf

Advances in Neural Information Processing Systems 16 (NIPS 2003) |

We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.