Transfer Learning via Feature Isomorphism Discovery

Transfer learning has gained increasing attention due to the inferior performance of machine learning algorithms with insufficient training data. Most of the previous homogeneous or heterogeneous transfer learning works aim to learn a mapping function between feature spaces based on the inherent correspondence across the source and target domains or labeled instances. However, in many real world applications, existing methods may not be robust when the correspondence across domains is noisy or labeled instances are not representative. In this paper, we develop a novel transfer learning framework called Transfer Learning via Feature Isomorphism Discovery (abbreviated to TLFid), which owns high tolerance for noisy correspondence between domains as well as scarce or non-existing labeled instances. More specifically, we propose a feature isomorphism approach to discovering common substructures across feature spaces and learning a feature mapping function from the target domain to the source domain. We evaluate the performance of TLFid on the cross-lingual sentiment classification tasks. The results show that our method achieves significant improvement in terms of accuracy compared with the state-of-the-art methods.