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