Modeling Sequential Domain Shift through Estimation of Optimal Sub-spaces for Categorization

Suranjana Samanta, Tirumarai Selvan and Sukhendu Das

In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.103

Abstract

This paper describes a new method of unsupervised domain adaptation (DA) using the properties of the sub-spaces spanning the source and target domains, when projected along a path in the Grassmann manifold. Our proposed method uses both the geometrical and the statistical properties of the subspaces spanning the two domains to estimate a sequence of optimal intermediary subspaces. This creates a path of shortest length between the sub-spaces of source and target domains, where the distributions of the projected source and target domain data are identical when projected onto these intermediate sub-spaces (lying along the path). We extend our concept to the kernel space and perform non-linear projections on the subspaces using kernel trick. Projections of the source and target domains onto these intermediary sub-spaces are used to obtain the incremental (or gradual) change in the geometrical as well as the statistical properties of sub-spaces spanning the source and target domains. Results on object and event categorization using real-world datasets, show that our proposed optimal path in the Grassmann manifold produces better results for the problem of DA than the usual geodesic path.

Session

Poster Session

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Bibtex File

Citation

Suranjana Samanta, Tirumarai Selvan, and Sukhendu Das. Modeling Sequential Domain Shift through Estimation of Optimal Sub-spaces for Categorization. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.

BibTex

@inproceedings{BMVC.28.103
	title = {Modeling Sequential Domain Shift through Estimation of Optimal Sub-spaces for Categorization},
	author = {Samanta, Suranjana and Selvan, Tirumarai and Das, Sukhendu},
	year = {2014},
	booktitle = {Proceedings of the British Machine Vision Conference},
	publisher = {BMVA Press},
	editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
	doi = { http://dx.doi.org/10.5244/C.28.103 }
}