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Published in:
2017 | OriginalPaper | Chapter
4. Unsupervised Domain Adaptation Based on Subspace Alignment
Authors : Basura Fernando, Rahaf Aljundi, Rémi Emonet, Amaury Habrard, Marc Sebban, Tinne Tuytelaars
Published in: Domain Adaptation in Computer Vision Applications
Publisher: Springer International Publishing
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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.