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Missing low-rank and sparse decomposition based on smoothed nuclear norm

Azghani, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TCSVT.2019.2907467
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. Recovering low-rank and sparse components from missing observations is an essential problem in various fields. In this paper, we have proposed a method to address the missing low-rank and sparse decomposition problem. We have used the smoothed nuclear norm and the L1 norm to impose the low-rankness and sparsity constraints on the components, respectively. Furthermore, we have suggested a linear modeling for the corrupted observations. The problem has been solved with the aid of alternating minimization. Moreover, some simplifications have been applied to the relations to reduce the computational complexity, which makes the algorithm suitable for large-scale problems. To evaluate the proposed method, different simulation scenarios have been devised. The superiority of the suggested scheme over its counterparts has been confirmed on both the recovery accuracy and the convergence speed in various applications. © 1991-2012 IEEE
  6. Keywords:
  7. Low-rank ; Low-rank and sparse decomposition ; Mmissing observation ; Sparsity ; Video signal processing ; Alternating minimization ; Convergence speed ; Essential problems ; Large-scale problem ; Linear modeling ; Low-rank and sparse decompositions ; Missing observations ; Sparsity constraints ; Networks (circuits)
  8. Source: IEEE Transactions on Circuits and Systems for Video Technology ; Volume 30, Issue 6 , 2020 , Pages 1550-1558
  9. URL: https://ieeexplore.ieee.org/document/8674582