Name
Abstract | ||
|---|---|---|
In this brief, a separable maximum correntropy criterion (SMCC) algorithm is developed by exploiting the typical separability property of tensors. Utilizing the separability property, a great number savings are obtained along with accelerated learning rate and improved estimate accuracy. In the proposed SMCC, a correntropy scheme is used to construct a adaptive algorithm to combat the impulsive noise and outliers in non-Gaussian environment. The complexity and convergence analysis of the SMCC are presented and discussed. Examples with two-way matrix and three-way tensor are carried out to verify the performance of the proposed SMCC algorithm under mixture Gaussian and Studentx2019;s t noises. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCSII.2020.2977608 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS |
Keywords | DocType | Volume |
Tensile stress, Signal processing algorithms, Convergence, Partitioning algorithms, Acceleration, Minimization, Computational complexity, Maximum correntropy criterion, tensor, separability, impulsive noise | Journal | 67 |
Issue | ISSN | Citations |
11 | 1549-7747 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wanlu Shi | 1 | 1 | 3.73 |
| Yingsong Li | 2 | 120 | 34.72 |
| Badong Chen | 3 | 919 | 65.71 |