Robust Subspace Recovery via Bi-Sparsity Pursuit
This addresses robust subspace recovery for computer vision and machine learning applications, representing an incremental improvement over existing sparse models.
The paper tackles the problem of recovering low-dimensional subspace structures from high-dimensional data corrupted by sparse errors and outliers, proposing a bi-sparse model and algorithm that demonstrates effectiveness on synthetic and real-world vision data.
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.