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Title: Data-Driven Approach to Multiple-Source Domain Adaptation
A key problem in domain adaptation is determining what to transfer across different domains. We propose a data-driven method to represent these changes across multiple source domains and perform unsupervised domain adaptation. We assume that the joint distributions follow a specific generating process and have a small number of identifiable changing parameters, and develop a data-driven method to identify the changing parameters by learning low-dimensional representations of the changing class-conditional distributions across multiple source domains. The learned low-dimensional representations enable us to reconstruct the target-domain joint distribution from unlabeled target-domain data, and further enable predicting the labels in the target domain. We demonstrate the efficacy of this method by conducting experiments on synthetic and real datasets.  more » « less
Award ID(s):
Author(s) / Creator(s):
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Page Range / eLocation ID:
3487 - 3496
Medium: X
Sponsoring Org:
National Science Foundation
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