  Robust principal component analysis (RPCA) is a powerful method for learning
low-rank feature representation of various visual data. However, for certain
types as well as significant amount of error corruption, it fails to yield
satisfactory results; a drawback that can be alleviated by exploiting
domain-dependent prior knowledge or information. In this paper, we propose two
models for the RPCA that take into account such side information, even in the
presence of missing values. We apply this framework to the task of UV
completion which is widely used in pose-invariant face recognition. Moreover,
we construct a generative adversarial network (GAN) to extract side information
as well as subspaces. These subspaces not only assist in the recovery but also
speed up the process in case of large-scale data. We quantitatively and
qualitatively evaluate the proposed approaches through both synthetic data and
five real-world datasets to verify their effectiveness.
