Date of Award
May 2016
Degree Type
Thesis
Degree Name
Master of Science
Department
Mathematics
First Advisor
Jugal Ghorai
Committee Members
Jugal Ghorai, Jay Beder, Anjishnu Banerjee
Abstract
Gaussian Process Regression is a non parametric approach for estimating relationships in data sets. For large data sets least square estimates are not feasible because of the covariance matrix inversion which requires O(n^3) computation. In Gaussian Process Regression a matrix inversion is also needed, but approximation methods exists for large n. Some of those approaches are studied in this thesis, among them are the random projection of the covariance matrix, Nyström method and the Johnson-Lindenstrauß Theorem. Furthermore sampling methods for Hyperparameter estimation are explored.
Recommended Citation
Kuhaupt, Nicolas, "Gaussian Process Regression for Large Data Sets" (2016). Theses and Dissertations. 1168.
https://dc.uwm.edu/etd/1168