Filtered Gaussian Processes for Learning with Large Data-Sets

Kernel-based non-parametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those methods to problems with large data-sets. In this paper we develop a filtering approach based on a Gaussian process regression model. The idea is to generate a smalldimensional set of filtered data that keeps a high proportion of the information contained in the original large data-set. Model learning and prediction are based on the filtered data, thereby decreasing the computational burden dramatically.

• Type: Conference
• Year: 2003
• Where: ESSMAC
• Authors: Jian Qing Shi, Roderick Murray-Smith, D. M. Titterington, Barak A. Pearlmutter
• Keywords: Complexity Imposes Limitations, ESSMAC 2003, Kernel-based Non-parametric Models, Large Data-set, Machine Learning