Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation

Rahaf Aljundi, Remi Emonet, Damien Muselet, Marc Sebban

CVPR 2015

doi:10.1109/CVPR.2015.7298600 /cvpr/2015/aljundi2015cvpr-landmarksbased/

Abstract

Domain adaptation (DA) has gained a lot of success in the recent years in computer vision to deal with situations where the learning process has to transfer knowledge from a source to a target domain. In this paper, we introduce a novel unsupervised DA approach based on both subspace alignment and selection of landmarks similarly distributed between the two domains. Those landmarks are selected so as to reduce the discrepancy between the domains and then are used to non linearly project the data in the same space where an efficient subspace alignment (in closed-form) is performed. We carry out a large experimental comparison in visual domain adaptation showing that our new method outperforms the most recent unsupervised DA approaches.

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Cite

Text

Aljundi et al. "Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298600

Markdown

[Aljundi et al. "Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/aljundi2015cvpr-landmarksbased/) doi:10.1109/CVPR.2015.7298600

BibTeX

@inproceedings{aljundi2015cvpr-landmarksbased,
  title     = {{Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation}},
  author    = {Aljundi, Rahaf and Emonet, Remi and Muselet, Damien and Sebban, Marc},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2015},
  doi       = {10.1109/CVPR.2015.7298600},
  url       = {https://mlanthology.org/cvpr/2015/aljundi2015cvpr-landmarksbased/}
}