amanpg: Alternating Manifold Proximal Gradient Method for Sparse PCA
Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal
Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides
a novel algorithm for solving the sparse principal component analysis problem which provides
advantages over existing methods in terms of efficiency and convergence guarantees.
Chen, S., Ma, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>.
Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>.
Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.
Version: |
0.3.4 |
Depends: |
R (≥ 3.5.0) |
Suggests: |
knitr, rmarkdown |
Published: |
2022-10-02 |
DOI: |
10.32614/CRAN.package.amanpg |
Author: |
Shixiang Chen [aut],
Justin Huang [aut],
Benjamin Jochem [aut],
Shiqian Ma [aut],
Haichuan Xu [aut],
Lingzhou Xue [aut],
Zhong Zheng [cre, aut],
Hui Zou [aut] |
Maintainer: |
Zhong Zheng <zvz5337 at psu.edu> |
License: |
MIT + file LICENSE |
NeedsCompilation: |
no |
Materials: |
README |
CRAN checks: |
amanpg results |
Documentation:
Downloads:
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