Unsupervised visual domain adaptation using subspace alignment

In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces described by eigenvectors. In this context, our method seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one. We show that the solution of the corresponding optimization problem can be obtained in a simple closed form, leading to an extremely fast algorithm. We use a theoretical result to tune the unique hyper parameter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrinsic simplicity, it outperforms state of the art DA methods. © 2013 IEEE. Fernando B., Habrard A., Sebban M., Tuytelaars T., ''Unsupervised visual domain adaptation using subspace alignment'', Proceedings 14th international conference on computer vision - ICCV 2013, pp. 2960-2967, December 3-6, 2013, Sydney, Australia. ispartof: pages:2960-2967 ispartof: Proceedings ICCV 2013 pages:2960-2967 ispartof: International conference on computer vision - ICCV 2013 location:Sydney, Australia date:3 Dec - 6 Dec 2013 status: published