  Domain adaptation aims to leverage knowledge from a well-labeled source
domain to a poorly-labeled target domain. A majority of existing works transfer
the knowledge at either feature level or sample level. Recent researches reveal
that both of the paradigms are essentially important, and optimizing one of
them can reinforce the other. Inspired by this, we propose a novel approach to
jointly exploit feature adaptation with distribution matching and sample
adaptation with landmark selection. During the knowledge transfer, we also take
the local consistency between samples into consideration, so that the manifold
structures of samples can be preserved. At last, we deploy label propagation to
predict the categories of new instances. Notably, our approach is suitable for
both homogeneous and heterogeneous domain adaptation by learning
domain-specific projections. Extensive experiments on five open benchmarks,
which consist of both standard and large-scale datasets, verify that our
approach can significantly outperform not only conventional approaches but also
end-to-end deep models. The experiments also demonstrate that we can leverage
handcrafted features to promote the accuracy on deep features by heterogeneous
adaptation.
