Compact Cluster-based Balanced Distribution Adaptation for Transfer Learning

Xu Zhang, Zuyu Zhang, Hae-Young Bae

Abstract: Recently, Domain Adaptation has received great attention in addressing domain shift problem, where source domain and target domain follow different distribution. Existing methods often seek to minimize the marginal distribution and the conditional distribution across domains however treat them equally, which is not reasonable in real applications. Moreover, it is easy to misclassify on target data with large within-class scatter. In this paper, we propose a Compact Cluster-based Balanced Distribution Adaptation (CC-BDA) method for cross-domain classification. CC-BDA exploits the balanced domain adaptation by increasing the balance factor dynamically in each iteration until convergence. In addition, we construct compact clusters to reduce within-class scatter alongside the domain adaptation, which endows samples with the same label much closer from their means. Finally, we present analysis and performance evaluation over seven state-of-the-art baseline methods. The results show that CC-BDA significantly outperforms other adaptation methods in accuracy and execution time cost.