Unsupervised double weighted domain adaptation

Domain adaptation can effectively transfer knowledge between domains with different distributions. Most existing methods use distribution alignment to mitigate the domain shift. But they typically align the marginal and conditional distributions with equal weights. This neglects the relative importance of different distribution alignments. In this paper, we propose a double weighted domain adaptation (DWDA) method, which employs new distribution alignment weighting and sample reweighting strategies. Specifically, the distribution alignment weighting strategy explores the relative importance of marginal and conditional distribution alignments, based on the maximum mean discrepancy; the sample reweighting strategy weights the source and target samples separately based on k-means clustering. The two strategies reinforce each other in the iterative optimization procedure, thus improving the overall performance. In addition, our method also considers the geometry structure preservation. The closed-form solution of the objective function is presented, and the computational complexity and convergence analysis are given. Experimental results demonstrate that DWDA outperforms state-of-the-art domain adaptation methods on several public datasets, and it also has good performance on an imbalanced dataset. Besides, DWDA is robust to a wide range of parameters. Moreover, the convergence curves show that DWDA generally converges rapidly within five iterations. We also evaluate the components of DWDA and show that each component is necessary. Finally, we compare the computational time with the methods of the recent three years to demonstrate the efficiency of DWDA.