Learning with local and global consistency

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Learning with local and global consistency (2004)

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

Venue:	Advances in Neural Information Processing Systems 16
Citations:	667 - 21 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

Keyphrases

global consistency unlabeled data semi-supervised learning classification problem demonstrates effective use principled approach method yield intrinsic structure transductive inference smooth solution simple algorithm general problem unlabeled point experimental result classifying function

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