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 BlahutArimoto-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.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Koji Tsuda
• Keywords: Closed Form, Exponential Family Distributions, ICML 2005, Information Regularization Framework, Machine Learning