  In this paper, we address the Online Unsupervised Domain Adaptation (OUDA)
problem, where the target data are unlabelled and arriving sequentially. The
traditional methods on the OUDA problem mainly focus on transforming each
arriving target data to the source domain, and they do not sufficiently
consider the temporal coherency and accumulative statistics among the arriving
target data. We propose a multi-step framework for the OUDA problem, which
institutes a novel method to compute the mean-target subspace inspired by the
geometrical interpretation on the Euclidean space. This mean-target subspace
contains accumulative temporal information among the arrived target data.
Moreover, the transformation matrix computed from the mean-target subspace is
applied to the next target data as a preprocessing step, aligning the target
data closer to the source domain. Experiments on four datasets demonstrated the
contribution of each step in our proposed multi-step OUDA framework and its
performance over previous approaches.
