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 sub-space 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 hyperparam-eter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrin-sic simplicity, it outperforms state of the art DA methods. 1