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This paper presents a novel unsupervised domain adaptation method forcross-domain visual recognition. We propose a unified framework that reducesthe shift between domains both statistically and geometrically, referred to asJoint Geometrical and Statistical Alignment (JGSA). Specifically, we learn twocoupled projections that project the source domain and target domain data intolow dimensional subspaces where the geometrical shift and distribution shiftare reduced simultaneously. The objective function can be solved efficiently ina closed form. Extensive experiments have verified that the proposed methodsignificantly outperforms several state-of-the-art domain adaptation methods ona synthetic dataset and three different real world cross-domain visualrecognition tasks.

Joint Geometrical and Statistical Alignment for Visual Domain Adaptation