Name
Abstract | ||
|---|---|---|
Robust principal component analysis (RPCA) has widely application in computer vision and data mining. However, the various RPCA algorithms in practical applications need to know the rank of low-rank matrix in advance, or adjust parameters. To overcome these limitations, an adaptive double-weighted RPCA algorithm is proposed to recover low-rank matrix accurately based on the estimated rank of the l... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCVW54120.2021.00024 | 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) |
Keywords | DocType | Volume |
Computer vision,Adaptation models,Conferences,Estimation,Sparse matrices,Data mining,Optimization | Conference | 2021 |
Issue | ISSN | ISBN |
1 | 2473-9936 | 978-1-6654-0191-3 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengqin Xu | 1 | 0 | 0.34 |
| Huasong Xing | 2 | 0 | 0.34 |
| Shun Fang | 3 | 0 | 0.68 |
| Shiqian Wu | 4 | 1347 | 85.75 |
| Shoulie Xie | 5 | 177 | 20.80 |