Learning with local and global consistency (2004)
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| Venue: | Advances in Neural Information Processing Systems 16 |
| Citations: | 286 - 20 self |
BibTeX
@INPROCEEDINGS{Zhou04learningwith,
author = {Dengyong Zhou and Olivier Bousquet and Thomas Navin Lal and Jason Weston and Bernhard Schölkopf},
title = {Learning with local and global consistency},
booktitle = {Advances in Neural Information Processing Systems 16},
year = {2004},
pages = {321--328},
publisher = {MIT Press}
}
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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. 1
