Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances

TitleGaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances
Publication TypeJournal Article
Year of Publication2013
AuthorsF. Pérez-Cruz, S. Van Vaerenbergh, J J. Murillo-Fuentes, M. Lázaro-Gredilla, and I. Santamaría
JournalIEEE Signal Processing Magazine
Volume30
Issue4
Pagination40-50
Month PublishedJuly
ISSN1053-5888
Keywordskernel adaptive filtering
AbstractGaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that 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 non-stationarity, low-complexity solutions, non-Gaussian noise models and classification scenarios. Furthermore, we provide a selection of relevant applications to wireless digital communications.
URLhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6530761
DOI10.1109/MSP.2013.2250352