  We present an algorithm that learns representations which explicitly
compensate for domain mismatch and which can be efficiently realized as linear
classifiers. Specifically, we form a linear transformation that maps features
from the target (test) domain to the source (training) domain as part of
training the classifier. We optimize both the transformation and classifier
parameters jointly, and introduce an efficient cost function based on
misclassification loss. Our method combines several features previously
unavailable in a single algorithm: multi-class adaptation through
representation learning, ability to map across heterogeneous feature spaces,
and scalability to large datasets. We present experiments on several image
datasets that demonstrate improved accuracy and computational advantages
compared to previous approaches.
