Bi-sparsity pursuit: A paradigm for robust subspace recovery
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.
- Authors:
-
- North Carolina State University, Raleigh, NC (United States)
- Publication Date:
- Research Org.:
- North Carolina State University, Raleigh, NC (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1614542
- Alternate Identifier(s):
- OSTI ID: 1548246
- Grant/Contract Number:
- NA0002576
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Signal Processing
- Additional Journal Information:
- Journal Volume: 152; Journal ID: ISSN 0165-1684
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; signal recovery; sparse learning; subspace modeling
Citation Formats
Bian, Xiao, Panahi, Ashkan, and Krim, Hamid. Bi-sparsity pursuit: A paradigm for robust subspace recovery. United States: N. p., 2018.
Web. doi:10.1016/j.sigpro.2018.05.024.
Bian, Xiao, Panahi, Ashkan, & Krim, Hamid. Bi-sparsity pursuit: A paradigm for robust subspace recovery. United States. https://doi.org/10.1016/j.sigpro.2018.05.024
Bian, Xiao, Panahi, Ashkan, and Krim, Hamid. Fri .
"Bi-sparsity pursuit: A paradigm for robust subspace recovery". United States. https://doi.org/10.1016/j.sigpro.2018.05.024. https://www.osti.gov/servlets/purl/1614542.
@article{osti_1614542,
title = {Bi-sparsity pursuit: A paradigm for robust subspace recovery},
author = {Bian, Xiao and Panahi, Ashkan and Krim, Hamid},
abstractNote = {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.},
doi = {10.1016/j.sigpro.2018.05.024},
journal = {Signal Processing},
number = ,
volume = 152,
place = {United States},
year = {Fri May 25 00:00:00 EDT 2018},
month = {Fri May 25 00:00:00 EDT 2018}
}
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Works referencing / citing this record:
Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion
journal, October 2020
- Ghanem, Sally; Panahi, Ashkan; Krim, Hamid
- IEEE Sensors Journal, Vol. 20, Issue 20