  In this paper, we propose the multi-domain dictionary learning (MDDL) to make
dictionary learning-based classification more robust to data representing in
different domains. We use adversarial neural networks to generate data in
different styles, and collect all the generated data into a miscellaneous
dictionary. To tackle the dictionary learning with many samples, we compute the
weighting matrix that compress the miscellaneous dictionary from multi-sample
per class to single sample per class. We show that the time complexity solving
the proposed MDDL with weighting matrix is the same as solving the dictionary
with single sample per class. Moreover, since the weighting matrix could help
the solver rely more on the training data, which possibly lie in the same
domain with the testing data, the classification could be more accurate.
