  Successful applications of sparse models in computer vision and machine
learning imply that in many real-world applications, high dimensional data is
distributed in a union of low dimensional subspaces. Nevertheless, the
underlying structure may be affected by sparse errors and/or outliers. In this
paper, we propose a bi-sparse model as a framework to analyze this problem and
provide a novel algorithm to recover the union of subspaces in presence of
sparse corruptions. We further show the effectiveness of our method by
experiments on both synthetic data and real-world vision data.
