Affiliations: [a] School of Information Science and Technology, Jiujiang University, Jiujiang, Jiangxi, China | [b] School of computer information and communication Engineering, Kunsan National University, South Korea
Correspondence:
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Corresponding author: Deng Pan, School of Information Science and Technology,Jiujiang University, Jiujiang, Jiangxi 332005, China. E-mail: [email protected].
Abstract: Domain adaptation is a method to classify the new domain accurately by using the marked image of the old domain. It shows a good but a challenging application prospect in computer vision. In this article, we propose a unified and optimized problem modeling method, which is called as Geodesic Kernel embedding Distribution Alignment (GKDA). Specifically, GKDA aims to reduce the domain differences. GKDA avoids degenerated feature transformation by using geodesic kernel mapping feature, and then adjusts the weight of cross-domain instances in the process of dimensionality reduction in principle, finally, constructs a new feature to represent the difference of distribution and unrelated instances. The experiment result shows that GKDA has obvious superiority in cross-domain image recognition.
Keywords: Domain adaptation, transfer learning, distribution alignment, geodesic kernel
