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All sparse PCA models are wrong, but some are useful. Part II: Limitations and problems of deflation

  • Published:2021-01 Issue: Volume:208 Page:104212
  • 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.,Bro Rasmus

Funder

Universidad de Granada

Ministerio de Economía y Competitividad

Publisher

Elsevier BV

Subject

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

Reference16 articles.

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

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

3. Statistical Learning with Sparsity: the Lasso and Generalizations;Trevor,2015

4. A selective overview of sparse principal component analysis;Zou;Proc. IEEE,2018

5. All sparse PCA models are wrong, but some are useful. Part I: computation of scores, residuals and explained variance;Camacho;Chemometr. Intell. Lab. Syst.,2020

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