Integration of Global and Local Metrics for Domain Adaptation Learning Via Dimensionality Reduction

Abstract

Domain adaptation learning (DAL) investigates how to perform a task across different domains. In this paper, we present a kernelized local-global approach to solve domain adaptation problems. The basic idea of the proposed method is to consider the global and local information regarding the domains (e.g., maximum mean discrepancy and intraclass distance) and to convert the domain adaptation problem into a bi-object optimization problem via the kernel method. A solution for the optimization problem will help us identify a latent space in which the distributions of the different domains will be close to each other in the global sense, and the local properties of the labeled source samples will be preserved. Therefore, classic classification algorithms can be used to recognize unlabeled target domain data, which has a significant difference on the source samples. Based on the analysis, we validate the proposed algorithm using four different sources of data: synthetic, textual, object, and facial image. The experimental results indicate that the proposed method provides a reasonable means to improve DAL algorithms.