Principal Component Analysis With Sparse Fused Loadings.

  title={Principal Component Analysis With Sparse Fused Loadings.},
  author={Jian Guo and Gareth M. James and Elizaveta Levina and George Michailidis and J I Zhu},
  journal={Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America},
  volume={19 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… CONTINUE READING

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