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Abstract
  • 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.
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