A Unified Framework for Domain Adaptation using Metric Learning on Manifolds

European Conference on Principles of Data Mining and Knowledge Discovery, Volume abs/1804.10834, 2018, Pages 843-860.

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We present a novel framework for domain adaptation, whereby both geometric and statistical differences between a labeled source domain and unlabeled target domain can be reconciled using a unified mathematical framework that exploits the curved Riemannian geometry of statistical manifolds. We exploit a simple but important observation tha...More



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