Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation

Rahaf Aljundi, Remi Emonet, Damien Muselet, Marc Sebban; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 56-63

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.

Related Material


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[bibtex]
@InProceedings{Aljundi_2015_CVPR,
author = {Aljundi, Rahaf and Emonet, Remi and Muselet, Damien and Sebban, Marc},
title = {Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}