Bi-sparsity pursuit: A paradigm for robust subspace recovery

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...

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Bibliographic Details
Published inSignal processing Vol. 152
Main Authors Bian, Xiao, Panahi, Ashkan, Krim, Hamid
Format Journal Article
LanguageEnglish
Published United States Elsevier 25.05.2018
Subjects
ENGINEERING
signal recovery
sparse learning
subspace modeling
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Summary: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.
Bibliography:NA0002576
USDOE National Nuclear Security Administration (NNSA)
ISSN:0165-1684
1872-7557