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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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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Abstract	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 variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
AbstractList	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 variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.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 variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application. 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 variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
Author	Cadima, Jorge Jolliffe, Ian T
Author_xml	– sequence: 1 givenname: Ian T surname: Jolliffe fullname: Jolliffe, Ian T organization: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK – sequence: 2 givenname: Jorge surname: Cadima fullname: Cadima, Jorge email: jcadima@isa.ulisboa.pt organization: Secção de Matemática (DCEB), Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa 1340-017, Portugal Centro de Estatística e Aplicações da Universidade de Lisboa (CEAUL), Lisboa, Portugal jcadima@isa.ulisboa.pt
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26953178$$D View this record in MEDLINE/PubMed
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