Learning with local and global consistency (2004) [115 citations — 16 self]

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

Advances in Neural Information Processing Systems 16

http://books.nips.cc/papers/files/nips16/NIPS2003_AA41.ps.gz

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@INPROCEEDINGS{Zhou04learningwith,
    author = {Dengyong Zhou and Olivier Bousquet and Thomas Navin Lal and Jason Weston and Bernhard Sch Olkopf},
    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

Citations

4514	Statistical Learning Theory – Vapnik - 1998
458	The architecture of cognition – Anderson - 1983
380	On spectral clustering: analysis and an algorithm – Ng, Jordan, et al. - 2002
132	Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions – Zhu, Ghahramani, et al. - 2003
102	Learning from labeled and unlabeled data using graph mincuts – Blum, Chawla - 2001
100	Partially labeled classification with markov random walks – Szummer, Jaakkola - 2001
98	Learning with Labeled and Unlabeled Data – Seeger - 2000
83	Transductive learning via spectral graph partitioning – Joachims
79	Diffusion kernels on graphs and other discrete input spaces – Kondor, Lafferty - 2002
75	Cluster kernels for semi-supervised learning – Chapelle, Weston, et al. - 1997
73	Training invariant support vector machines – DeCoste, Schölkopf - 2001
55	Kernels and regularization on graphs – Smola, Kondor - 2003
23	Problems of Learning on Manifolds, The – Belkin - 2003
22	Learning Semantic Similarity – Kandola, Taylor, et al. - 2003
12	Observation of phase transitions in spreading activation networks – Shrager, Hogg, et al. - 1987

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