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All sparse PCA models are wrong, but some are useful. Part I: Computation of scores, residuals and explained variance

  • Published:2020-01 Issue: Volume:196 Page:103907
  • ISSN:0169-7439
  • Container-title:Chemometrics and Intelligent Laboratory Systems
  • language:en
  • Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Camacho J.,Smilde A.K.,Saccenti E.,Westerhuis J.A.

Funder

Ministry of Economy and Competitiveness

FEDER

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference26 articles.

1. A modified principal component technique based on the LASSO;Jolliffe;J. Comput. Graph. Stat.,2003

2. Sparse principal component analysis;Zou;J. Comput. Graph. Stat.,2006

3. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis;Witten Daniela;Biostatistics,2009

4. Group-wise principal component analysis for exploratory data analysis;José;J. Comput. Graph. Stat.,2017

5. Deflation methods for sparse PCA;Lester;NIPS (News Physiol. Sci.),2008

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