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: Linking:Please use the canonical form https://CRAN.R-project.org/package=amanpg to link to this page.
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