Chunk-wise regularised PCA-based imputation of missing data
Chunk-wise regularised PCA-based imputation of missing data
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Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
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Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
Standard multivariate techniques like Principal Component Analysis (PCA) are based on the eigendecomposition of a matrix and therefore require complete data sets. Recent comparative reviews of PCA algorithms for missing data showed the regularised iterative PCA algorithm (RPCA) to be effective. This paper presents two chunk-wise implementations of...
Alternative Titles
Full title
Chunk-wise regularised PCA-based imputation of missing data
Authors, Artists and Contributors
Author / Creator
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TN_cdi_proquest_journals_2668323361
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2668323361
Other Identifiers
ISSN
1618-2510
E-ISSN
1613-981X
DOI
10.1007/s10260-021-00575-5
