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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Vydané v:	Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Ročník 374; číslo 2065; s. 20150202
Hlavní autori:	Jolliffe, Ian T, Cadima, Jorge
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England 13.04.2016
Predmet:	palaeontology dimension reduction multivariate analysis eigenvectors
ISSN:	1471-2962, 1471-2962
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