Name
Abstract | ||
|---|---|---|
The tensor completion issues have obtained a great deal of attention in the past few years. However, the data fidelity part minimizes a squared loss function, which may be inappropriate for the case of noisy one-bit observations. In this paper, we alleviate the mentioned difficulty by drawing on the experience of matrix scenarios. Based on the convex relation to ℓ norm of the tensor multi-rank, we... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIP.2018.2865837 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Tensors,Minimization,Optimization,Numerical models | Applied mathematics,Fidelity,Square (algebra),Numerical models,Pattern recognition,Tensor,Matrix (mathematics),Tensor completion,Stress (mechanics),Regular polygon,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
28 | 1 | 1057-7149 |
Citations | PageRank | References |
2 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baohua Li | 1 | 34 | 5.57 |
| Xiaoning Zhang | 2 | 35 | 2.72 |
| Xiaoli Li | 3 | 76 | 11.26 |
| Huchuan Lu | 4 | 4827 | 186.26 |