Multi-source Transfer Learning Method by Balancing both the Domains and Instances

When transfer learning attempts to leverage the decision knowledge effectively from multiple source domains to predict the labels of instances accurately in target domain, it should consider how to well balance source and target domains, and their instances in both domains.In this paper, a novel multi-source transfer learning method called mlti-source transfer learning by balancing both domains and instances (MTL-BDI) is proposed to achieve the above goal.The basic idea of the proposed method is to embed the doubly weighted domain-level and instance-level balance term into the original objective function of transfer learning and then solve the proposed objective function effectively by using the alternating optimization technique.Extensive experiments on text and image datasets indicate that the proposed method indeed outperforms several existing multi-source transfer learning methods MCC-SVM (Multiple Convex Combination of SVM), A-SVM (Adaptive SVM), Multi-KMM (Multiple Kernel Mean Matching) and DAM (Domain Adaptation Machine) in the sense of classification accuracy on target domain. © 2019, Chinese Institute of Electronics. All right reserved.