Learning with Local and Global Consistency

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Dengyong Zhou, Olivier Bousquet, Thomas Lal, Jason Weston, Bernhard Schölkopf


We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive in- ference. A principled approach to semi-supervised learning is to design a classifying function which is suf(cid:2)ciently 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 clas- si(cid:2)cation problems and demonstrates effective use of unlabeled data.