Learning Infinite Hidden Relational Models

Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinite-state latent variable as part of a Dirichlet process (DP) mixture model. It can be viewed as a relational generalization of hidden Markov random field. The information propagates in the intern-connected network via latent variables, reducing the necessary for extensive structure learning. For inference, we explore a Gibbs sampling method based on the Chinese restaurant process. The performance of our model is demonstrated in three applications: the movie recommendation, the function prediction of genes and a medical recommendation system.