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Propagating Distributions on a Hypergraph by Dual Information Regularization

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84265

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

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Zitation

Tsuda, K. (2005). Propagating Distributions on a Hypergraph by Dual Information Regularization. In ICML Bonn (pp. 921).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D6E7-C
Zusammenfassung
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.