2018 journal article
Bi-sparsity pursuit: A paradigm for robust subspace recovery
Signal Processing, 152, 148β159.
The success of sparse models in computer vision and machine learning is due to the fact that, high dimensional data is distributed in a union of low dimensional subspaces in many real-world applications. The underlying structure may, however, be adversely affected by sparse errors. 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 the presence of sparse corruptions. We further show the effectiveness of our method by experiments on real-world vision data.