Bi-sparsity pursuit: A paradigm for robust subspace recovery
Journal Article
·
· Signal Processing
- North Carolina State University, Raleigh, NC (United States)
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.
- Research Organization:
- North Carolina State University, Raleigh, NC (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- NA0002576
- OSTI ID:
- 1614542
- Alternate ID(s):
- OSTI ID: 1548246
- Journal Information:
- Signal Processing, Vol. 152; ISSN 0165-1684
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Cited by: 3 works
Citation information provided by
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Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion
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