  In practice, the data distribution at test time often differs, to a smaller
or larger extent, from that of the original training data. Consequentially, the
so-called source classifier, trained on the available labelled data,
deteriorates on the test, or target, data. Domain adaptive classifiers aim to
combat this problem, but typically assume some particular form of domain shift.
Most are not robust to violations of domain shift assumptions and may even
perform worse than their non-adaptive counterparts. We construct robust
parameter estimators for discriminant analysis that guarantee performance
improvements of the adaptive classifier over the non-adaptive source
classifier.
