Propagating Distributions on a Hypergraph by Dual Information Regularization

Tsuda, K

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Propagating Distributions on a Hypergraph by Dual Information Regularization

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Tsuda, K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://dl.acm.org/citation.cfm?doid=1102351.1102467
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Citation

Tsuda, K. (2005). Propagating Distributions on a Hypergraph by Dual Information Regularization. In S. Dzeroski, L. De Raedt, & S. Wrobel (Eds.), ICML '05: 22nd International Conference on Machine Learning (pp. 920-927). New York, NY, USA: ACM Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D6E7-C

Abstract

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.