Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6957134 | Signal Processing | 2018 | 32 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Xiao Bian, Ashkan Panahi, Hamid Krim,