Abstract
The central idea of principal component analysis (PCA) is to reduce the dimensionality of a data set which consists of a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This is achieved by transforming to a new set of variables, the principal components (PCs), which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables.
Keywords
- Principal Component Analysis
- Lagrange Multiplier
- Original Variable
- Large Eigenvalue
- Multivariate Normal Distribution
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1986 Springer Science+Business Media New York
About this chapter
Cite this chapter
Jolliffe, I.T. (1986). Introduction. In: Principal Component Analysis. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-1904-8_1
Download citation
DOI: https://doi.org/10.1007/978-1-4757-1904-8_1
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-1906-2
Online ISBN: 978-1-4757-1904-8
eBook Packages: Springer Book Archive