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Learning from Labeled and Unlabeled Data Using Random Walks

2004

Conference Paper

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We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to predict the labels of the unlabeled points. Any supervised learning algorithm can be applied to this problem, for instance, Support Vector Machines (SVMs). The problem of our interest is if we can implement a classifier which uses the unlabeled data information in some way and has higher accuracy than the classifiers which use the labeled data only. Recently we proposed a simple algorithm, which can substantially benefit from large amounts of unlabeled data and demonstrates clear superiority to supervised learning methods. In this paper we further investigate the algorithm using random walks and spectral graph theory, which shed light on the key steps in this algorithm.

Author(s): Zhou, D. and Schölkopf, B.
Journal: Pattern Recognition, Proceedings of the 26th DAGM Symposium
Pages: 237-244
Year: 2004
Day: 0
Editors: Rasmussen, C.E., H.H. B{\"u}lthoff, M.A. Giese and B. Sch{\"o}lkopf

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Pattern Recognition, Proceedings of the 26th DAGM Symposium

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2684,
  title = {Learning from Labeled and Unlabeled Data Using Random Walks},
  author = {Zhou, D. and Sch{\"o}lkopf, B.},
  journal = {Pattern Recognition, Proceedings of the 26th DAGM Symposium},
  pages = {237-244},
  editors = {Rasmussen, C.E., H.H. B{\"u}lthoff, M.A. Giese and B. Sch{\"o}lkopf},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2004}
}