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Data-Driven Approach to Multiple-Source Domain Adaptation.

ABSTRACT: 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.

SUBMITTER: Stojanov P

PROVIDER: S-EPMC6730632 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Data-Driven Approach to Multiple-Source Domain Adaptation.

Stojanov Petar P Gong Mingming M Carbonell Jaime G JG Zhang Kun K

Proceedings of machine learning research 20190401

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-c ...[more]

PMID: 31497777

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