Extreme Learning Machine Based on Maximum Weighted Mean Discrepancy for Unsupervised Domain Adaptation

Abstract

Extreme Learning Machine (ELM) has shown fast learning speed and good generalization property in single-domain problems, such as classification and regression. However, the assumption that the training and testing data are subject to identical distribution often leads to significant performance degradation of ELM in cross-domain problems. To cope with unsupervised domain adaptation problems by ELM, we propose a novel method called Extreme Learning Machine based on Maximum Weighted Mean Discrepancy (ELM-MWMD) in this paper, which learns an adaptive ELM classifier with both labeled source data and unlabeled target data. Firstly, the cross-domain weight coefficients are specifically designed and assigned for each sample in source and target domains, fully considering the effects of individual information. Then the source classifier is adapted to the target domain by minimizing the distribution discrepancy between the two domains, both the marginal distribution and conditional distribution are simultaneously reduced to obtain a more accurate target classifier. Moreover, the predicted results for target data are utilized as pseudo labels to further improve the classification accuracy in multiple iterations. Extensive experiments on public image datasets demonstrate that ELM-MWMD performs better than several existing state-of-the-art domain adaptation methods by computation efficiency and classification accuracy.