Ensemble Metric Learning trains multiple distance metric learners — each on a different data view, feature subspace, or with a different objective — and combines the resulting metrics to produce a single, more robust similarity function. Combining diverse metrics reduces the variance of any individual metric and improves performance in tasks such as nearest-neighbor classification, retrieval, and few-shot learning.
Wang, J., Kalousis, A., & Woznica, A. (2012). Parametric local metric learning for nearest neighbor classification. Advances in Neural Information Processing Systems, 25. link ↗