Common feature extraction in multi-source domains for transfer learning

@article{Tahmoresnezhad2015CommonFE,
  title={Common feature extraction in multi-source domains for transfer learning},
  author={Jafar Tahmoresnezhad and Sattar Hashemi},
  journal={2015 7th Conference on Information and Knowledge Technology (IKT)},
  year={2015},
  pages={1-5}
}
In transfer learning scenarios, finding a common feature representation is crucial to tackle the problem of domain shift where the training (source domain) and test (target domain) sets have difference in their distribution. However, classical dimensionality reduction approaches such as Fisher Discriminant Analysis (FDA), are not in good yields whenever dealing with shift problem. In this paper we introduce CoMuT, a Common feature extraction in Multi-source domains for Transfer learning, that… CONTINUE READING