Exploratory Principal Component Analysis
epca is an R package for comprehending any data matrix that contains
low-rank and sparse underlying signals of interest. The package
currently features two key tools:
scafor sparse principal component analysis.
smafor sparse matrix approximation, a two-way data analysis for simultaneously row and column dimensionality reductions.
You can install the released version of epca from CRAN with:
or the development version from GitHub with:
# install.packages("devtools") devtools::install_github("fchen365/epca")
The usage of
sma is straightforward. For example, to find
k sparse PCs of a data matrix
Similarly, we can find a rank-
k sparse matrix decomposition by
For more examples, please see the vignette:
If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.
Chen F and Rohe K, “A New Basis for Sparse PCA.” (arXiv)