A transductive transfer learning approach for image classification

Among machine learning paradigms, unsupervised transductive transfer learning is useful when no labeled data from the target domain are available at training time, but there is accessible unlabeled target data during training phase instead. The current paper proposes a novel unsupervised transductive transfer learning method to find the specific and shared features across the source and the target domains. The proposed learning method then maps both domains into the respective subspaces with minimum marginal and conditional distribution divergences. It is shown that the discriminative learning across domains leads to boost the model performance. Hence, the proposed method discriminates the classes of both domains via maximizing the distance between each sample-pairs with different labels and via minimizing the distance between each instance-pairs of the same classes. We verified our approach using standard visual benchmarks, with the average accuracy of 46 experiments as 76.5%, which rates rather high in comparison with other state-of-the-art transfer learning methods through various cross-domain tasks.