Bi-sparsity pursuit: A paradigm for robust subspace recovery

Bian, Xiao; Panahi, Ashkan; Krim, Hamid

doi:10.1016/j.sigpro.2018.05.024

Bi-sparsity pursuit: A paradigm for robust subspace recovery

Journal Article · Fri May 25 00:00:00 EDT 2018 · Signal Processing

DOI:https://doi.org/10.1016/j.sigpro.2018.05.024· OSTI ID:1614542

Bian, Xiao ^[1]; ^[2]; Krim, Hamid ^[2]

North Carolina State University, Raleigh, NC (United States); DOE/OSTI
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.

View Accepted Manuscript (DOE)

Research Organization:: North Carolina State Univ., 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, Journal Name: Signal Processing Vol. 152; ISSN 0165-1684

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited By (1)

Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion Ghanem, Sally; Panahi, Ashkan; Krim, Hamid IEEE Sensors Journal, Vol. 20, Issue 20 https://doi.org/10.1109/jsen.2020.2999461	journal	October 2020

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