  Robust principal component analysis (RPCA) has drawn significant attentions
due to its powerful capability in recovering low-rank matrices as well as
successful appplications in various real world problems. The current
state-of-the-art algorithms usually need to solve singular value decomposition
of large matrices, which generally has at least a quadratic or even cubic
complexity. This drawback has limited the application of RPCA in solving real
world problems. To combat this drawback, in this paper we propose a new type of
RPCA method, RES-PCA, which is linearly efficient and scalable in both data
size and dimension. For comparison purpose, AltProj, an existing scalable
approach to RPCA requires the precise knowlwdge of the true rank; otherwise, it
may fail to recover low-rank matrices. By contrast, our method works with or
without knowing the true rank; even when both methods work, our method is
faster. Extensive experiments have been performed and testified to the
effectiveness of proposed method quantitatively and in visual quality, which
suggests that our method is suitable to be employed as a light-weight, scalable
component for RPCA in any application pipelines.
