Scalable metric learning for co-embedding

Abstract

We present a general formulation of metric learning for coembedding, where the goal is to relate objects from different sets. The framework allows metric learning to be applied to a wide range of problems—including link prediction, relation learning, multi-label tagging and ranking—while allowing training to be reformulated as convex optimization. For training we provide a fast iterative algorithm that improves the scalability of existing metric learning approaches. Empirically, we demonstrate that the proposed method converges to a global optimum efficiently, and achieves competitive results in a variety of co-embedding problems such as multi-label classification and multirelational prediction.