A Novel Low-rank and Sparse Decomposition Algorithm Based on Laplacian Distribution

The principal component pursuit (PCP) method has an excellent performance in foreground/background separation, but this method is also acknowledged to have some drawbacks: 1) the poor robustness; 2) the choice of balancing parameters is a tricky matter. To address these problems, we propose a new lo...

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Bibliographic Details
Published in2022 4th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP) pp. 969 - 972
Main Authors Fan, Ruibo, Jing, Mingli, Chen, Tengfei, Liu, Wanchun
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.07.2022
Subjects
Approximation algorithms
foreground/background separation
Laplace equations
Laplacian scale mixture
low-rank matrix decomposition
Matrix decomposition
Minimization
Robustness
Signal processing
Signal processing algorithms
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Summary:The principal component pursuit (PCP) method has an excellent performance in foreground/background separation, but this method is also acknowledged to have some drawbacks: 1) the poor robustness; 2) the choice of balancing parameters is a tricky matter. To address these problems, we propose a new low-rank and sparse decomposition model based on the nuclear norm and Laplacian scale mixture. This model uses the Laplacian scale mixture to approximate the sparse term to improve the robustness of PCP and reduce the difficulty of adjusting parameters. Experimental results show that our approach is more effective than the PCP algorithm.
DOI:10.1109/ICMSP55950.2022.9858949