Any R implementation for dimension reduction using random projection?

Question

I have a large p (~20K) and small n (~500) problem. The first thing I was thinking is dimension reduction. After trying PCA, robust PCA, ICA, removing highly correlated features, I was thinking to use Random Projection. However, there is no simple R implementation of Random Projection.

I have found a few random projection R packages, like

1. MCLUST: https://cran.r-project.org/web/packages/mclust/index.html
2. RPEnsemble: https://cran.r-project.org/web/packages/RPEnsemble/index.html

But, it seems that they don't support random projection directly for dimension reduction. I have limited knowledge about random projection, but I found two functions in sklearn support this:

Gaussian random projection and Sparse random projection: http://scikit-learn.org/stable/modules/random_projection.html

And it has pretty simple function interface.

Is that possible to implement random projection easily in R? Or, taking advantage of existing tools to do dimension reduction with Random Projection in R?

Answer 1

I concur that the RPEnsemble package doesn't seem to expose the low-level methods that would allow you to use only that feature in any convenient form.

I did however come across this R source code which seems fairly straight-forward and reasonably documented: R source code for random projections. This is part of the clusterv package and you can download it there.