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Principal Component Analysis With Sparse Fused Loadings.

ABSTRACT: In this article, we propose a new method for principal component analysis (PCA), whose main objective is to capture natural "blocking" structures in the variables. Further, the method, beyond selecting different variables for different components, also encourages the loadings of highly correlated variables to have the same magnitude. These two features often help in interpreting the principal components. To achieve these goals, a fusion penalty is introduced and the resulting optimization problem solved by an alternating block optimization algorithm. The method is applied to a number of simulated and real datasets and it is shown that it achieves the stated objectives. The supplemental materials for this article are available online.

SUBMITTER: Guo J

PROVIDER: S-EPMC4394907 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Principal Component Analysis With Sparse Fused Loadings.

Guo Jian J James Gareth G Levina Elizaveta E Michailidis George G Zhu Ji J

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20100101 4

In this article, we propose a new method for principal component analysis (PCA), whose main objective is to capture natural "blocking" structures in the variables. Further, the method, beyond selecting different variables for different components, also encourages the loadings of highly correlated variables to have the same magnitude. These two features often help in interpreting the principal components. To achieve these goals, a fusion penalty is introduced and the resulting optimization proble ...[more]

PMID: 25878487

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