  A predictor, $f_A : X \to Y$, learned with data from a source domain (A)
might not be accurate on a target domain (B) when their distributions are
different. Domain adaptation aims to reduce the negative effects of this
distribution mismatch. Here, we analyze the case where $P_A(Y\ |\ X) \neq
P_B(Y\ |\ X)$, $P_A(X) \neq P_B(X)$ but $P_A(Y) = P_B(Y)$; where there are
affine transformations of $X$ that makes all distributions equivalent. We
propose an approach to project the source and target domains into a
lower-dimensional, common space, by (1) projecting the domains into the
eigenvectors of the empirical covariance matrices of each domain, then (2)
finding an orthogonal matrix that minimizes the maximum mean discrepancy
between the projections of both domains. For arbitrary affine transformations,
there is an inherent unidentifiability problem when performing unsupervised
domain adaptation that can be alleviated in the semi-supervised case. We show
the effectiveness of our approach in simulated data and in binary digit
classification tasks, obtaining improvements up to 48% accuracy when correcting
for the domain shift in the data.
