Adaptive component embedding for unsupervised domain adaptation

Domain adaptation has obtained considerable interest from the literatures of multimedia, especially in cross-domain knowledge transfer problems. In this paper, we propose an effective yet time-saving approach, named Adaptive Component Embedding (ACE), for unsupervised domain adaptation. Specifically, ACE learns adaptive components across domains to embed all data in a shared subspace where the distribution divergence is mitigated and the underlying geometric structures in the local manifold are preserved. Then, an adaptive classifier is learned by using Representer Theorem in the Reproducing Kernel Hilbert Space (RKHS). The objective of our method can be efficiently solved in a closed form. Comprehensive experiments on both standard and large-scale datasets verify that ACE significantly outperforms previous state-of-the-art methods in terms of the classification accuracy and training time