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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Scalable Metric Learning for Co-Embedding
verfasst von : Farzaneh Mirzazadeh, Martha White, András György, Dale Schuurmans
Erschienen in: Machine Learning and Knowledge Discovery in Databases
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We present a general formulation of metric learning for co-embedding, 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 multi-relational prediction.