Principal component analysis: a review and recent developments

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated varia...

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Bibliographic Details

Published in:	Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 374; no. 2065; p. 20150202
Main Authors:	Jolliffe, Ian T, Cadima, Jorge
Format:	Journal Article
Language:	English
Published:	England 13.04.2016
Subjects:	palaeontology dimension reduction multivariate analysis eigenvectors
ISSN:	1471-2962, 1471-2962
Online Access:	Get more information
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