Propagating Distributions on a Hypergraph by Dual Information Regularization
2005
Conference Paper
ei
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}, doi = {} } |