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Principal Component Analysis

  • Ron WehrensEmail author
Chapter
Part of the Use R book series (USE R)

Abstract

Principal Component Analysis (PCA, [24, 25]) is a technique which, quite literally, takes a di_erent viewpoint of multivariate data. In fact, PCA de_nes new variables, consisting of linear combinations of the original ones, in such a way that the _rst axis is in the direction containing most variation. Every subsequent new variable is orthogonal to previous variables, but again in the direction containing most of the remaining variation. The new variables are examples of what often is called latent variables (LVs), and in the context of PCA they are also termed principal components (PCs).

Keywords

Independent Component Analysis Independent Component Analysis Scree Plot Principal Coordinate Analysis Factor Analysis Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Research and Innovation CentreFondazione Edmund MachSan Michele all’AdigeItaly

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