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Unsupervised Domain Adaptation Based on Subspace Alignment

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Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Subspace-based domain adaptation methods have been very successful in the context of image recognition. In this chapter, we discuss methods using Subspace Alignment (SA). They are based on a mapping function which aligns the source subspace with the target one, so as to obtain a domain invariant feature space. The solution of the corresponding optimization problem can be obtained in closed form, leading to a simple to implement and fast algorithm. The only hyperparameter involved corresponds to the dimension of the subspaces. We give two methods, SA and SA-MLE, for setting this variable. SA is a purely linear method. As a nonlinear extension, Landmarks-based Kernelized Subspace Alignment (LSSA) first projects the data nonlinearly based on a set of landmarks, which have been selected so as to reduce the discrepancy between the domains.

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  • DOI: 10.1007/978-3-319-58347-1_4
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Fig. 4.1

Notes

  1. 1.

    We experimented with several regularization methods on the transformation matrix M such as 2-norm, trace norm, and Frobenius norm regularization. None of these regularization strategies improved over using no regularization.

Acknowledgements

The authors gratefully acknowledge support from the FP7 ERC Starting Grant 240530 COGNIMUND, and the ANR projects SOLSTICE (ANR-13-BS02-01) and LIVES (ANR-15-CE23-0026-03).

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Correspondence to Rémi Emonet .

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Fernando, B., Aljundi, R., Emonet, R., Habrard, A., Sebban, M., Tuytelaars, T. (2017). Unsupervised Domain Adaptation Based on Subspace Alignment. In: Csurka, G. (eds) Domain Adaptation in Computer Vision Applications. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-58347-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-58347-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58346-4

  • Online ISBN: 978-3-319-58347-1

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