  Sparse coding has shown its power as an effective data representation method.
However, up to now, all the sparse coding approaches are limited within the
single domain learning problem. In this paper, we extend the sparse coding to
cross domain learning problem, which tries to learn from a source domain to a
target domain with significant different distribution. We impose the Maximum
Mean Discrepancy (MMD) criterion to reduce the cross-domain distribution
difference of sparse codes, and also regularize the sparse codes by the class
labels of the samples from both domains to increase the discriminative ability.
The encouraging experiment results of the proposed cross-domain sparse coding
algorithm on two challenging tasks --- image classification of photograph and
oil painting domains, and multiple user spam detection --- show the advantage
of the proposed method over other cross-domain data representation methods.
