Gaussian processes for nonlinear signal processing Articles uri icon

authors

  • PEREZ CRUZ, FERNANDO
  • VAN VAERENBERGH, STEVEN
  • MURILLO FUENTES, JUAN JOSE
  • LAZARO GREDILLA, MIGUEL
  • SANTAMARIA, IGNACIO

publication date

  • July 2013

start page

  • 40

end page

  • 50

issue

  • 4

volume

  • 30

International Standard Serial Number (ISSN)

  • 1053-5888

Electronic International Standard Serial Number (EISSN)

  • 1558-0792

abstract

  • Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning but are rarely used in signal processing. In this tutorial, we present GPs for regression as a natural nonlinear extension to optimal Wiener filtering. After establishing their basic formulation, we discuss several important aspects and extensions, including recursive and adaptive algorithms for dealing with nonstationarity, low-complexity solutions, non-Gaussian noise models, and classification scenarios. Furthermore, we provide a selection of relevant applications to wireless digital communications.