Introduction

Abstract

The main purpose of principal-components analysis is to reduce the dimensionality of multivariate data to make its structure clearer. It does this by looking for the linear combination of the variables which accounts for as much as possible of the total variation in the data. It then goes on to look for a second combination, uncorrelated with the first, which accounts for as much of the remaining variation as possible - and so on. If the greater part of the variation is accounted for by a small number of components, they may be used in place of the original variables. © 2010 Elsevier Ltd. All rights reserved.