Heterogeneous Domain Adaptation Using Manifold Alignment

Wang, Chang; Mahadevan, Sridhar

doi:10.5591/978-1-57735-516-8/IJCAI11-259

Heterogeneous Domain Adaptation Using Manifold Alignment

Chang Wang, Sridhar Mahadevan

Download BibTeX Cite

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1541-1545.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-259

Abstract

We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications.

Chang Wang, and Sridhar Mahadevan. 2011. Heterogeneous Domain Adaptation Using Manifold Alignment. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1541–1545. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-259

@inproceedings{ijcai2011p259,
  title     = {Heterogeneous Domain Adaptation Using Manifold Alignment},
  author    = {Wang, Chang and Mahadevan, Sridhar},
  booktitle = {Proceedings of the Twenty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-11}},
  publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
  editor    = {Toby Walsh},
  pages     = {1541--1545},
  year      = {2011},
  month     = {7},
  doi       = {10.5591/978-1-57735-516-8/IJCAI11-259},
  url       = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-259},
}

TY  - CPAPER
TI  - Heterogeneous Domain Adaptation Using Manifold Alignment
AU  - Wang, Chang
AU  - Mahadevan, Sridhar
T2  - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11
PB  - AAAI Press/International Joint Conferences on Artificial Intelligence
PY  - 2011
SP  - 1541
EP  - 1545
DO  - 10.5591/978-1-57735-516-8/IJCAI11-259
UR  - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-259
ER  -