Heterogeneous domain adaptation by Features Normalization and Data Topology Preserving

摘要

Transfer Learning (TL) algorithms are effective methods for utilizing the source domain knowledge to improve classifier learning in the target domain. These algorithms use labeled source domain instances to reinforce learning in the new target domain. The challenging model of these algorithms, i.e. heterogeneous domain adaptation, is characterized by the source and target domains, which have different features spaces, different data distributions and labels. Two effective factors for improving the performance of TL algorithms are reducing the difference in feature space and distribution between domains. Recently, some TL methods have focused on reducing the difference in distribution and some on reducing the difference in feature space between domains, and a few number of methods have considered the two issues, simultaneously. However, these methods usually use complex computational structures, such as deep neural networks and optimization methods, to adapt the feature space and domain distribution. Another important factor for increasing the efficiency of TL algorithms is preserving topology of data during transfer between domains that have not been studied in the existing TL algorithms. Simultaneous use of these three factors in a single framework improve the performance of TL algorithms. To this end, this paper proposes a novel method for solving heterogeneous domain adaptation problems based on Feature Normalization and Data Topology Preserving (FN-DTP). FN-DTP employs the feature normalization technique in the source and target domains. Hence, the feature spaces of the source and target domains closes together and reduces the difference in the distribution of domain data, while simultaneously preserve topology of data during transfer between domains. This method, without using complex computational structures, employs the mentioned three factors to improve the performance of TL algorithms in a unified framework. Experimental evaluation by using the Office-Caltech and PIE benchmark datasets demonstrates the effectiveness and efficiency of the proposed method compared with the state-of-the-art method in improving learning in semi-supervised classifications.