Learning with Local and Global Consistency

Abstract

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.

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@inproceedings{Zhou2003LearningWL, title={Learning with Local and Global Consistency}, author={Dengyong Zhou and Olivier Bousquet and Thomas Navin Lal and Jason Weston and Bernhard Sch{\"{o}lkopf}, booktitle={NIPS}, year={2003} }