PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multi-Relational Learning with Gaussian Processes
Zhao Xu, Kristian Kersting and Volker Tresp
In: The 21st International Joint Conference on Artificial Intelligence (IJCAI-09), 11-17 JULY 2009, Pasadena, CA, USA.


Due to their flexible nonparametric nature, Gaussian process models are very effective at solving hard machine learning problems. While existing Gaussian process models focus on modeling one single relation, we present a generalized GP model, named multi-relational Gaussian process model, that is able to deal with an arbitrary number of relations in a domain of interest. The proposed model is analyzed in the context of bipartite, directed, and undirected univariate relations. Experimental results on real-world datasets show that exploiting the correlations among different entity types and relations can indeed improve prediction performance.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6679
Deposited By:Zhao Xu
Deposited On:08 March 2010