Hybrid Heterogeneous Transfer Learning through Deep Learning


  • Joey Zhou Nanyang Technological University
  • Sinno Pan Institute for Infocomm Research
  • Ivor Tsang University of Technology, Sydney
  • Yan Yan University of Queensland




Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between heterogeneous feature spaces based on a few cross-domain instance-correspondences, and these corresponding instances are assumed to be representative in the source and target domains respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be precisely due to the bias issue of the correspondences in the target or (and) source domain(s). In this case, a classifier trained on the labeled transformed-source-domain data may not be useful for the target domain. In this paper, we present a new transfer learning framework called Hybrid Heterogeneous Transfer Learning (HHTL), which allows the corresponding instances across domains to be biased in either the source or target domain. Specifically, we propose a deep learning approach to learn a feature mapping between cross-domain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross-domain correspondences. Extensive experiments on several multilingual sentiment classification tasks verify the effectiveness of our proposed approach compared with some baseline methods.




How to Cite

Zhou, J., Pan, S., Tsang, I., & Yan, Y. (2014). Hybrid Heterogeneous Transfer Learning through Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8961



Main Track: Novel Machine Learning Algorithms