A Transductive Transfer Learning Approach Based on Manifold Learning

Traditional machine learning approaches usually assume that the training data and the test data follow the same distribution. When the training data and the test data are not drawn from the same distribution, most traditional machine learning approaches cannot work well. Transfer learning approaches allow the distributions of the training data and the test data to be different. Transfer learning approaches mainly focus on how to reduce the difference between the training data and the test data, but mostly ignore the potential semantic information. This paper proposes a transductive transfer learning approach based on manifold learning (TTL-ML). TTL-ML designs a new feature representation space, where the semantic information hidden in the dataset structure is preserved, and the distribution difference between the source domain and the target domain is reduced. Experimental results on the common transfer learning datasets demonstrate that the proposed approach outperforms the several state-of-the-art approaches on image classification.