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.
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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|Item Type:||Conference Paper|
|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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