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:
sca
for sparse principal component analysis.sma
for 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:
install.packages("epca")
or the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("fchen365/epca")
The usage of sca
and sma
is straightforward. For example, to find
k
sparse PCs of a data matrix X
:
sca(X, k)
Similarly, we can find a rank-k
sparse matrix decomposition by
sma(X, k)
For more examples, please see the vignette:
vignette("epca")
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)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.