Name
Abstract | ||
|---|---|---|
In this paper we propose a novel Bayesian kernel based solution for regression in complex fields. We develop the formulation of the Gaussian process for regression (GPR) to deal with complex-valued outputs. Previous solutions for kernels methods usually assume a complexification approach, where the real-valued kernel is replaced by a complex-valued one. However, based on the results in complex-valued linear theory, we prove that both a kernel and a pseudo-kernel are to be included in the solution. This is the starting point to develop the new formulation for the complex-valued GPR. The obtained formulation resembles the one of the widely linear minimum mean-squared (WLMMSE) approach. Just in the particular case where the outputs are proper, the pseudo-kernel cancels and the solution simplifies to a real-valued GPR structure, as the WLMMSE does into a strictly linear solution. We include some numerical experiments to show that the novel solution, denoted as widely non-linear complex GPR (WCGPR), outperforms a strictly complex GPR where a pseudo-kernel is not included. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Kernel (linear algebra),Mathematical optimization,Nonlinear system,Ground-penetrating radar,Regression,Linear system,Artificial intelligence,Gaussian process,Mathematics,Machine learning,Bayesian probability |
DocType | Volume | Citations |
Journal | abs/1511.05710 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael Boloix-Tortosa | 1 | 42 | 7.20 |
| Eva Arias-de-Reyna | 2 | 5 | 1.89 |
| F. Javier Payan-Somet | 3 | 9 | 1.94 |
| Juan Jose Murillo-Fuentes | 4 | 0 | 1.35 |