  Current supervised learning models cannot generalize well across domain
boundaries, which is a known problem in many applications, such as robotics or
visual classification. Domain adaptation methods are used to improve these
generalization properties. However, these techniques suffer either from being
restricted to a particular task, such as visual adaptation, require a lot of
computational time and data, which is not always guaranteed, have complex
parameterization, or expensive optimization procedures. In this work, we
present an approach that requires only a well-chosen snapshot of data to find a
single domain invariant subspace. The subspace is calculated in closed form and
overrides domain structures, which makes it fast and stable in
parameterization. By employing low-rank techniques, we emphasize on descriptive
characteristics of data. The presented idea is evaluated on various domain
adaptation tasks such as text and image classification against state of the art
domain adaptation approaches and achieves remarkable performance across all
tasks.
