Manifold Alignment Preserving Global Geometry
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1743-1748.
Abstract
This paper proposes a novel algorithm for manifold alignment preserving global geometry. This approach constructs mapping functions that project data instances from different input domains to a new lower-dimensional space, simultaneously matching the instances in correspondence and preserving global distances between instances within the original domains. In contrast to previous approaches, which are largely based on preserving local geometry, the proposed approach is suited to applications where the global manifold geometry needs to be respected. We evaluate the effectiveness of our algorithm for transfer learning in two real-world cross-lingual information retrieval tasks.
@inproceedings{ijcai2013p258,
title = {Manifold Alignment Preserving Global Geometry},
author = {Wang, Chang and Mahadevan, Sridhar},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {1743--1748},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/258},
}
TY - CPAPER TI - Manifold Alignment Preserving Global Geometry AU - Wang, Chang AU - Mahadevan, Sridhar T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 1743 EP - 1748 UR - https://ijcai.org/proceedings/2013/258 ER -Copied
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