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- W2530762997 abstract "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 its combination with a non-linear DR which allows to adapt to more complex data characterised by a global non-linear structure." @default.
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- W2530762997 date "2015-01-01" @default.
- W2530762997 modified "2023-09-23" @default.
- W2530762997 title "Unsupervised Dimensionality Reduction for Transfer Learning" @default.
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