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Semi-supervised Learning via Generalized Maximum Entropy

2010

Conference Paper

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Various supervised inference methods can be analyzed as convex duals of the generalized maximum entropy (MaxEnt) framework. Generalized MaxEnt aims to find a distribution that maximizes an entropy function while respecting prior information represented as potential functions in miscellaneous forms of constraints and/or penalties. We extend this framework to semi-supervised learning by incorporating unlabeled data via modifications to these potential functions reflecting structural assumptions on the data geometry. The proposed approach leads to a family of discriminative semi-supervised algorithms, that are convex, scalable, inherently multi-class, easy to implement, and that can be kernelized naturally. Experimental evaluation of special cases shows the competitiveness of our methodology.

Author(s): Erkan, AN. and Altun, Y.
Book Title: JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010
Journal: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)
Pages: 209-216
Year: 2010
Month: May
Day: 0
Editors: Teh, Y.W. , M. Titterington
Publisher: JMLR

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

Event Name: Thirteenth International Conference on Artificial Intelligence and Statistics
Event Place: Chia Laguna Resort, Italy

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{6622,
  title = {Semi-supervised Learning via Generalized Maximum Entropy},
  author = {Erkan, AN. and Altun, Y.},
  journal = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010},
  pages = {209-216},
  editors = {Teh, Y.W. , M. Titterington},
  publisher = {JMLR},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = may,
  year = {2010},
  month_numeric = {5}
}