Unsupervised Domain Adaptation Based on Subspace Alignment
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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.
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).