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