About Open Access
Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data
Lookup NU author(s)
Dr Gang Yi
Dr Jian Shi
Yi G, Shi JQ, Choi T
Full text is available for this publication:
Full text file 1
The model based on Gaussian process (GP) prior and a kernel covariance function can be used to fit nonlinear data with multidimensional covariates. It has been used as a flexible nonparametric approach for curve fitting, classification, clustering, and other statistical problems, and has been widely applied to deal with complex nonlinear systems in many different areas particularly in machine learning. However, it is a challenging problem when the model is used for the large-scale data sets and high-dimensional data, for example, for the meat data discussed in this article that have 100 highly correlated covariates. For such data, it suffers from large variance of parameter estimation and high predictive errors, and numerically, it suffers from unstable computation. In this article, penalized likelihood framework will be applied to the model based on GPs. Different penalties will be investigated, and their ability in application given to suit the characteristics of GP models will be discussed. The asymptotic properties will also be discussed with the relevant proofs. Several applications to real biomechanical and bioinformatics data sets will be reported.
Wiley-Blackwell Publishing Ltd.
Newcastle University Library, NE2 4HQ, United Kingdom. Tel: 0044 (191) 222 7657
©2015 Newcastle University Library