Domain Adaptation on the Statistical Manifold

Baktashmotlagh, M., Harandi, Mehrtash T., Lovell, Brian C., & Salzmann, M. (2014) Domain Adaptation on the Statistical Manifold. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 23, 28 2014, Greater Columbus Convention Center, Columbus, Ohio, USA.

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In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions be- come similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that prob- ability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this man- ifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the cor- responding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of- the-art results on a standard object recognition benchmark.

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4 citations in Scopus
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4 citations in Web of Science®

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ID Code: 90969
Item Type: Conference Paper
Refereed: Yes
Keywords: Domain Adaptation, Statistical Manifold, Invariant Embedding, Object Recognition
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright the Author
Deposited On: 03 Dec 2015 03:05
Last Modified: 04 Dec 2015 04:10

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