@inproceedings{Boerm.Garcke:2007,
author = {S. B\"orm and J. Garcke},
title = {Approximating Gaussian Processes with ${H^2}$-matrices},
booktitle = {Proceedings of 18th European Conference on Machine
Learning, Warsaw, Poland, September 17-21, 2007. ECML
2007},
year = {2007},
editor = {Joost N. Kok and Jacek Koronacki and Ramon Lopez de
Mantaras and Stan Matwin and Dunja Mladen and Andrzej
Skowron},
volume = {4701},
pages = {42--53},
abstract = {To compute the exact solution of Gaussian process
regression one needs $\Os(N^3)$ computations for direct and
$\Os(N^2)$ for iterative methods since it involves a
densely populated kernel matrix of size $N \times N$, here
$N$ denotes the number of data. This makes large scale
learning problems intractable by standard techniques. We
propose to use an alternative approach: the kernel matrix
is replaced by a data-sparse approximation, called an
${\mathcal H}^2$-matrix. This matrix can be represented by
only ${\cal O}(N m)$ units of storage, where $m$ is a
parameter controlling the accuracy of the approximation,
while the computation of the ${\mathcal H}^2$-matrix scales
with ${\cal O}(N m \log N)$. Practical experiments
demonstrate that our scheme leads to significant reductions
in storage requirements and computing times for large data
sets in lower dimensional spaces.},
annote = {proc_ref},
doi = {10.1007/978-3-540-74958-5_8},
file = {gpWithH2.pdf:http\://www.math.tu-berlin.de/~garcke/paper/gpWithH2.pdf:PDF},
pdf = {http://garcke.ins.uni-bonn.de/research/pub/gpWithH2.pdf 1},
seriestitle = { Lecture Notes in Artificial Intelligence}
}