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.