We present a general framework for association learning, where entities are embedded in a common latent space to express relatedness via geometry-an approach that underlies the state of the art for link prediction, relation learning, multi-label tagging, relevance retrieval and ranking. Although current approaches rely on local training methods applied to non-convex formulations, we demonstrate how general convex formulations can be achieved for entity embedding, both for standard multi-linear and prototype-distance models. We investigate an efficient optimization strategy that allows scaling. An experimental evaluation reveals the advantages of global training in different case studies.

28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
School of Computer Science

Mirzazadeh, F. (Farzaneh), Guo, Y, & Schuurmans, D. (Dale). (2014). Convex co-embedding. In Proceedings of the National Conference on Artificial Intelligence (pp. 1989–1996).