  Domain adaptation aims to assist the modeling tasks of the target domain with
knowledge of the source domain. The two domains often lie in different feature
spaces due to diverse data collection methods, which leads to the more
challenging task of heterogeneous domain adaptation (HDA). A core issue of HDA
is how to preserve the information of the original data during adaptation. In
this paper, we propose a joint information preservation method to deal with the
problem. The method preserves the information of the original data from two
aspects. On the one hand, although paired samples often exist between the two
domains of the HDA, current algorithms do not utilize such information
sufficiently. The proposed method preserves the paired information by
maximizing the correlation of the paired samples in the shared subspace. On the
other hand, the proposed method improves the strategy of preserving the
structural information of the original data, where the local and global
structural information are preserved simultaneously. Finally, the joint
information preservation is integrated by distribution matching. Experimental
results show the superiority of the proposed method over the state-of-the-art
HDA algorithms.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning