We investigate the suitability of unsupervised dimensionality

reduction (DR) for transfer learning in the context of different representations

of the source and target domain. Essentially, unsupervised DR

establishes a link of source and target domain by representing the data in

a common latent space. We consider two settings: a linear DR of source

and target data which establishes correspondences of the data and an according

transfer, and its combination with a non-linear DR which allows to

adapt to more complex data characterised by a global non-linear structure.