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Title: BI-sparsity pursuit for robust subspace recovery

Abstract

Here, the success of sparse models in computer vision and machine learning in many real-world applications, may be attributed in large part, to the fact that many high dimensional data are distributed in a union of low dimensional subspaces. The underlying structure may, however, be adversely affected by sparse errors, thus inducing additional complexity in recovering it. In this paper, we propose a bi-sparse model as a framework to investigate and analyze this problem, and provide as a result , a novel algorithm to recover the union of subspaces in presence of sparse corruptions. We additionally demonstrate the effectiveness of our method by experiments on real-world vision data.

Authors:
 [1];  [1]
  1. North Carolina State Univ., Raleigh, NC (United States)
Publication Date:
Research Org.:
North Carolina State University, Raleigh, NC (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
OSTI Identifier:
1438406
Grant/Contract Number:  
NA0002576
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Conference: IEEE conference on Image Processing (ICIP)
Additional Journal Information:
Journal Name: Conference: IEEE conference on Image Processing (ICIP)
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; non-convex optimization; Sparse representation; signal recovery; video segmentation; face clustering

Citation Formats

Bian, Xiao, and Krim, Hamid. BI-sparsity pursuit for robust subspace recovery. United States: N. p., 2015. Web. doi:10.1109/ICIP.2015.7351462.
Bian, Xiao, & Krim, Hamid. BI-sparsity pursuit for robust subspace recovery. United States. https://doi.org/10.1109/ICIP.2015.7351462
Bian, Xiao, and Krim, Hamid. 2015. "BI-sparsity pursuit for robust subspace recovery". United States. https://doi.org/10.1109/ICIP.2015.7351462. https://www.osti.gov/servlets/purl/1438406.
@article{osti_1438406,
title = {BI-sparsity pursuit for robust subspace recovery},
author = {Bian, Xiao and Krim, Hamid},
abstractNote = {Here, the success of sparse models in computer vision and machine learning in many real-world applications, may be attributed in large part, to the fact that many high dimensional data are distributed in a union of low dimensional subspaces. The underlying structure may, however, be adversely affected by sparse errors, thus inducing additional complexity in recovering it. In this paper, we propose a bi-sparse model as a framework to investigate and analyze this problem, and provide as a result , a novel algorithm to recover the union of subspaces in presence of sparse corruptions. We additionally demonstrate the effectiveness of our method by experiments on real-world vision data.},
doi = {10.1109/ICIP.2015.7351462},
url = {https://www.osti.gov/biblio/1438406}, journal = {Conference: IEEE conference on Image Processing (ICIP)},
number = ,
volume = ,
place = {United States},
year = {Tue Sep 01 00:00:00 EDT 2015},
month = {Tue Sep 01 00:00:00 EDT 2015}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Fig. 1 Fig. 1: An example of robust subspace exact recovery.

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Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.