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Propagating Distributions on a Hypergraph by Dual Information Regularization

2005

Conference Paper

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In the information regularization framework by Corduneanu and Jaakkola (2005), the distributions of labels are propagated on a hypergraph for semi-supervised learning. The learning is efficiently done by a Blahut-Arimoto-like two step algorithm, but, unfortunately, one of the steps cannot be solved in a closed form. In this paper, we propose a dual version of information regularization, which is considered as more natural in terms of information geometry. Our learning algorithm has two steps, each of which can be solved in a closed form. Also it can be naturally applied to exponential family distributions such as Gaussians. In experiments, our algorithm is applied to protein classification based on a metabolic network and known functional categories.

Author(s): Tsuda, K.
Journal: Proceedings of the 22nd International Conference on Machine Learning
Pages: 921
Year: 2005
Day: 0
Editors: De Raedt, L. , S. Wrobel

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

Event Name: ICML Bonn

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

BibTex

@inproceedings{3468,
  title = {Propagating Distributions on a Hypergraph by Dual Information Regularization},
  author = {Tsuda, K.},
  journal = {Proceedings of the 22nd International Conference on Machine Learning},
  pages = {921 },
  editors = {De Raedt, L. , S. Wrobel},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2005}
}