Unsupervised Dimensionality Reduction for Transfer Learning

We investigate the suitability of unsupervised dimensionality reduction (DR) for transfer learning in the context of different represen- tations 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 ac- cording transfer, and...