@article{Perez-Cruz2013,

title = {Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances},

author = {Fernando Perez-Cruz and Steven Van Vaerenbergh and Juan Jose Murillo-Fuentes and Miguel Lazaro-Gredilla and Ignacio Santamaria},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6530761},

issn = {1053-5888},

year  = {2013},

date = {2013-01-01},

journal = {IEEE Signal Processing Magazine},

volume = {30},

number = {4},

pages = {40--50},

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.},

keywords = {adaptive algorithm, Adaptive algorithms, classification scenario, Gaussian processes, Learning systems, Machine learning, Noise measurement, nonGaussian noise model, Nonlinear estimation, nonlinear estimation problem, nonlinear signal processing, optimal Wiener filtering, recursive algorithm, Signal processing, Wiener filters, wireless digital communication},

pubstate = {published},

tppubtype = {article}

}