Joint Domain Matching and Classification for cross-domain adaptation via ELM

Abstract

Recent years, domain adaptation has attracted much attention in the community of machine learning. In this paper, we mainly focus on the tasks of Joint Domain Matching and Classification (JDMC) under the framework of extreme learning machine (ELM). Specifically, our JDMC method is formulated by optimizing both the output-adapted transformation and the cross-domain classifier, which allows us to simultaneously (1) align the source domain and target domain in the feature space with correlation alignment, (2) minimize the discrepancy between the source and target domain, measured in terms of both marginal and conditional probability distribution in the mapped feature space, (3) select informative features which behave similarly in both domains for knowledge transfer by imposing ℓ2,1-norm on the output weights of ELM. In this respect, the proposed JDMC integrates the feature matching, feature selection and classifier design in a unified framework. Besides, an efficient alternative optimization strategy is exploited to solve the joint learning model. To evaluate the effectiveness of the proposed method, extensive experiments on several commonly used domain adaptation datasets are presented, the results show that the proposed method significantly outperforms the non-transfer ELM networks and consistently outperforms several state-of-art domain adaptation methods.