Sparse Spectrum Gaussian Process Regression

9 years 20 days ago
Sparse Spectrum Gaussian Process Regression
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algorithm for regression tasks. We compare the achievable trade-offs between predictive accuracy and computational requirements, and show that these are typically superior to existing state-of-the-art sparse approximations. We discuss both the weight space and function space representations, and note that the new construction implies priors over functions which are always stationary, and can approximate any covariance function in this class.
Miguel Lázaro-Gredilla, Joaquin Quiñ
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal
Comments (0)