Unconstrained fuzzy feature fusion for heterogeneous unsupervised domain adaptation

Publication Type:
Conference Proceeding
Citation:
IEEE International Conference on Fuzzy Systems, 2018, 2018-July
Issue Date:
2018-10-12
Filename Description Size
PID5276861.pdfAccepted Manuscript489.28 kB
Adobe PDF
Full metadata record
© 2018 IEEE. Domain adaptation can transfer knowledge from the source domain to improve pattern recognition accuracy in the target domain. However, it is rarely discussed when the target domain is unlabeled and heterogeneous with the source domain, which is a very challenging problem in the domain adaptation field. This paper presents a new feature reconstruction method: unconstrained fuzzy feature fusion. Through the reconstructed features of a source and a target domain, a geodesic flow kernel is applied to transfer knowledge between them. Furthermore, the original information of the target domain is also preserved when reconstructing the features of the two domains. Compared to the previous work, this work has two advantages: 1) the sum of the memberships of the original features to fuzzy features no longer must be one, and 2) the original information of the target domain is persevered. As a result of these advantages, this work delivers a better performance than previous studies using two public datasets.
Please use this identifier to cite or link to this item: