Joint Feature Selection and Structure Preservation for Domain Adaptation
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1697-1702.
Abstract
The essence of domain adaptation is to explore common latent factors shared by the involved domains. These factors can be specific features or geometric structures. Most of previous methods exploit either the shared features or the shared geometric structures separately. However, the two strategies are complementary with each other and jointly exploring them is more optimal. This paper proposes a novel approach, named joint Feature Selection and Structure Preservation (FSSP), for unsupervised domain adaptation. FSSP smoothly integrates structure preservation and feature selection into a unified optimization problem. Intensive experiments on text categorization, image classification and video event recognition demonstrate that our method performs better, even with up to 30% improvement in average, compared with the state-of-the-art methods.
@inproceedings{ijcai2016p243,
title = {Joint Feature Selection and Structure Preservation for Domain Adaptation},
author = {Li, Jingjing and Zhao, Jidong and Lu, Ke},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {1697--1702},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/243},
}
TY - CPAPER TI - Joint Feature Selection and Structure Preservation for Domain Adaptation AU - Li, Jingjing AU - Zhao, Jidong AU - Lu, Ke T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 1697 EP - 1702 UR - https://ijcai.org/proceedings/2016/243 ER -Copied
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