Name
Abstract | ||
|---|---|---|
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 mo... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCSVT.2019.2907467 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Sparse matrices,Matrix decomposition,Minimization,Lagrangian functions,Computational complexity,Computational modeling,Signal processing algorithms | Journal | 30 |
Issue | ISSN | Citations |
6 | 1051-8215 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masoumeh Azghani | 1 | 18 | 6.17 |
| Ashkan Esmaeili | 2 | 7 | 2.59 |
| Kayhan Behdin | 3 | 0 | 0.34 |
| Farokh Marvasti | 4 | 573 | 72.71 |