Imola K. Fodor,
A survey of dimension reduction techniques.
Tech. Rep. UCRL-ID-148494, Center for Applied Scientific Computing, Lawrence
Livermore National Laboratory, P.O. Box 808, L-560, Livermore, CA 94551, USA, June 2002.
Imola K. Fodor,
A survey of dimension reduction techniques.
Tech. Rep. UCRL-ID-148494, Center for Applied Scientific Computing, Lawrence
Livermore National Laboratory, P.O. Box 808, L-560, Livermore, CA 94551, USA, June 2002.
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.