This paper introduces a new method to solve the cross-domain recognition problem. Different from the traditional domain adaption methods which rely on a global domain shift for all classes between source and target domain, the proposed method is more flexible to capture individual class variations across domains. We propose to solves the problem by finding the compact joint subspaces of source and target domain. We evaluate the proposed method on two widely used datasets and comparison results demonstrates that the proposed method outperforms the comparison methods. © 2015 IEEE.