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Group-sparse block PCA and explained variance

Abstract : The paper addresses the simultneous determination of goup-sparse loadings by block optimization, and the correlated problem of defining explained variance for a set of non orthogonal components. We give in both cases a comprehensive mathematical presentation of the problem, which leads to propose i) a new formulation/algorithm for group-sparse block PCA and ii) a framework for the definition of explained variance with the analysis of five definitions. The numerical results i) confirm the superiority of block optimization over deflation for the determination of group-sparse loadings, and the importance of group information when available, and ii) show that ranking of algorithms according to explained variance is essentially independant of the definition of explained variance. These results lead to propose a new optimal variance as the definition of choice for explained variance.
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Contributor : Marie Chavent <>
Submitted on : Wednesday, December 13, 2017 - 12:02:40 PM
Last modification on : Monday, May 25, 2020 - 10:56:13 AM

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  • HAL Id : hal-01662605, version 1
  • ARXIV : 1705.00461




Marie Chavent, Guy Chavent. Group-sparse block PCA and explained variance. 2017. ⟨hal-01662605⟩



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