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Quantifying the estimation error of principal component vectors

Abstract:

Principal component analysis is an important pattern recognition and dimensionality reduction tool in many applications. Principal components are computed as eigenvectors of a maximum likelihood covariance $\widehat{\Sigma}$ that approximates a population covariance $\Sigma$, and these eigenvectors are often used to extract structural information about the variables (or attributes) of the studied population. Since PCA is based on the eigendecomposition of the proxy covariance $\widehat{\Sigma...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1093/imaiai/iaz014

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
Pembroke College
Role:
Author
ORCID:
0000-0003-4549-9047
Publisher:
Oxford University Press Publisher's website
Journal:
Information and Inference Journal website
Article number:
iaz014
Publication date:
2019-07-11
Acceptance date:
2019-05-26
DOI:
EISSN:
2049-8772
ISSN:
2049-8764
Source identifiers:
871251
Language:
English
Keywords:
Pubs id:
pubs:871251
UUID:
uuid:3679a50c-07ab-4d5e-ae1e-325e0901a4e9
Local pid:
pubs:871251
Deposit date:
2019-06-03

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