Complex-Valued Gaussian Processes for Regression: A Widely Non-Linear Approach

@article{BoloixTortosa2015ComplexValuedGP,
  title={Complex-Valued Gaussian Processes for Regression: A Widely Non-Linear Approach},
  author={Rafael Boloix-Tortosa and Eva Arias-de-Reyna and F. Javier Payan-Somet and Juan Jos{\'e} Murillo-Fuentes},
  journal={ArXiv},
  year={2015},
  volume={abs/1511.05710}
}
  • Rafael Boloix-Tortosa, Eva Arias-de-Reyna, +1 author Juan José Murillo-Fuentes
  • Published in ArXiv 2015
  • Mathematics, Computer Science
  • 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… CONTINUE READING

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