Unsupervised Dimensionality Reduction for Transfer Learning
Patrick Blöbaum, Alexander Schulz, Barbara Hammer · PUB – Publications at Bielefeld University (Bielefeld University) · 2015
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.