Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.
The DESCRIPTION file:
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This package contains
DDPCA_convex function, which decomposes a positive semidefinite matrix into a low rank component, and a diagonally dominant component using either nonconvex approach or convex approach.
Please cite the reference paper to cite this R package.
Tracy Ke [aut], Lingzhou Xue [aut], Fan Yang [aut, cre]
Maintainer: Fan Yang <[email protected]>
Ke, Z., Xue, L. and Yang, F., 2019. Diagonally Dominant Principal Component Analysis. Journal of Computational and Graphic Statistics, under review.
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