  Unsupervised Domain Adaptation (UDA) addresses the problem of performance
degradation due to domain shift between training and testing sets, which is
common in computer vision applications. Most existing UDA approaches are based
on vector-form data although the typical format of data or features in visual
applications is multi-dimensional tensor. Besides, current methods, including
the deep network approaches, assume that abundant labeled source samples are
provided for training. However, the number of labeled source samples are always
limited due to expensive annotation cost in practice, making sub-optimal
performance been observed. In this paper, we propose to seek discriminative
representation for multi-dimensional data by learning a structured dictionary
in tensor space. The dictionary separates domain-specific information and
class-specific information to guarantee the representation robust to domains.
In addition, a pseudo-label estimation scheme is developed to combine with
discriminant analysis in the algorithm iteration for avoiding the external
classifier design. We perform extensive results on different datasets with
limited source samples. Experimental results demonstrates that the proposed
method outperforms the state-of-the-art approaches.
