Name
Title | ||
|---|---|---|
Complex Correntropy Induced Metric Applied to Compressive Sensing with Complex-Valued Data |
Abstract | ||
|---|---|---|
The correntropy induced metric (CIM) is a well-defined metric induced by the correntropy function and has been applied to different problems in signal processing and machine learning, but CIM was limited to the case of real-valued data. This paper extends the CIM to the case of complex- valued data, denoted by Complex Correntropy Induced Metric (CCIM). The new metric preserves the well known benefits of extracting high order statistical information from correntropy, but now dealing with complex-valued data. As an example, the paper shows the CCIM applied in the approximation of ℓ
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>
-minimization in the reconstruction of complex-valued sparse signals in a compressive sensing problem formulation. A mathematical proof is presented as well as simulation results that indicate the viability of the proposed new metric. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SSIAI.2018.8470371 | 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) |
Keywords | Field | DocType |
Approximation to ℓ0,complex correntropy induced metric,complex-valued data,compressive sensing | Kernel (linear algebra),Iterative reconstruction,Signal processing,Pattern recognition,Computer science,Robustness (computer science),Minification,Mathematical proof,Induced metric,Artificial intelligence,Compressed sensing | Conference |
ISSN | ISBN | Citations |
1550-5782 | 978-1-5386-6569-5 | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| João P. F. Guimarães | 1 | 5 | 2.48 |
| Aluisio I. R. Fontes | 2 | 20 | 4.50 |
| Felipe B. da Silva | 3 | 0 | 0.34 |
| Allan de M. Martins | 4 | 4 | 0.74 |
| Ricardo von Borries | 5 | 43 | 4.89 |