Name
Abstract | ||
|---|---|---|
In this paper, we introduce a novel and robust approach to quantized matrix completion. First, we propose a rank minimization problem with constraints induced by quantization bounds. Next, we form an unconstrained optimization problem by regularizing the rank function with Huber loss. Huber loss is leveraged to control the violation from quantization bounds due to two properties: first, it is diff... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LSP.2019.2891134 | IEEE Signal Processing Letters |
Keywords | DocType | Volume |
Quantization (signal),Approximation algorithms,Signal processing algorithms,Estimation,Convergence,Loss measurement,Minimization | Journal | 26 |
Issue | ISSN | Citations |
2 | 1070-9908 | 1 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashkan Esmaeili | 1 | 7 | 2.59 |
| Farokh Marvasti | 2 | 573 | 72.71 |