Propagating Distributions on a Hypergraph by Dual Information Regularization

@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},
  author = {Tsuda, K.}
}