@InProceedings{ Garcke.Griebel:2001*1,
author = {J. Garcke and M. Griebel},
title = {On the parallelization of the sparse grid approach for
data mining},
booktitle = {Large-Scale Scientific Computations, Third International
Conference, LSSC 2001, Sozopol, Bulgaria},
series = {Lecture Notes in Computer Science},
volume = 2179,
pages = {22-32},
publisher = {Springer},
editor = {S. Margenov and J. Wasniewski and P. Yalamov},
ps = {http://wissrech.ins.uni-bonn.de/research/pub/garcke/psm.ps.gz}
,
pdf = {http://wissrech.ins.uni-bonn.de/research/pub/garcke/psm.pdf}
,
abstract = {Recently we presented a new approach to the classification
problem arising in data mining. It is based on the
regularization network approach, but in contrast to other
methods which employ ansatz functions associated to data
points, we use basis functions coming from a grid in the
usually high-dimensional feature space for the minimization
process. To cope with the curse of dimensionality, we
employ sparse grids. To be precise we use the sparse grid
combination technique where the classification problem is
discretized and solved on a sequence of conventional grids
with uniform mesh sizes in each dimension. The sparse grid
solution is then obtained by linear combination. The method
scales linearly with the number of data points and is well
suited for data mining applications where the amount of
data is very large, but where the dimension of the feature
space is moderately high.
The computation on each grid of the sequence of grids is
independent of each other and therefore can be done in
parallel already on a coarse grain level. A second level of
parallelization on a fine grain level can be introduced on
each grid through the use of threading on shared-memory
multi-processor computers.
We describe the sparse grid combination technique for the
classification problem, the two ways of parallelisation,
and report on the results on a 10 dimensional data set. },
year = {2001},
annote = {series,other}
}