2002
@techreport{Fod2002,
vgclass = {report},
author = {Imola K. Fodor},
title = {A survey of dimension reduction techniques},
number = {UCRL-ID-148494},
institution = {Center for Applied Scientific Computing, Lawrence
Livermore National Laboratory},
address = {P.O. Box 808, L-560, Livermore, CA 94551, USA},
month = {June},
year = {2002},
url = {http://www.llnl.gov/CASC/sapphire/pubs/148494.pdf},
abstract = {Advances in data collection and storage capabilities
during the past decades have led to an information overload in most
sciences. Researchers working in domains as diverse as engineering,
astronomy, biology, remote sensing, economics, and consumer
transactions, face larger and larger observations and simulations on a
daily basis. Such datasets, in contrast with smaller, more traditional
datasets that have been studied extensively in the past, present new
challenges in data analysis. Traditional statistical methods break down
partly because of the increase in the number of observations, but
mostly because of the increase in the number of variables associated
with each observation. The dimension of the data is the number of
variables that are measured on each observation.},
}