  Machine learning models in practical settings are typically confronted with
changes to the distribution of the incoming data. Such changes can severely
affect the model performance, leading for example to misclassifications of
data. This is particularly apparent in the domain of bionic hand prostheses,
where machine learning models promise faster and more intuitive user
interfaces, but are hindered by their lack of robustness to everyday
disturbances, such as electrode shifts. One way to address changes in the data
distribution is transfer learning, that is, to transfer the disturbed data to a
space where the original model is applicable again. In this contribution, we
propose a novel expectation maximization algorithm to learn linear
transformations that maximize the likelihood of disturbed data after the
transformation. We also show that this approach generalizes to discriminative
models, in particular learning vector quantization models. In our evaluation on
data from the bionic prostheses domain we demonstrate that our approach can
learn a transformation which improves classification accuracy significantly and
outperforms all tested baselines, if few data or few classes are available in
the target domain.
