I could not reply directly to the initial thread with the same title.

There are two sorts of Robust PCA, those that were devised before the
recent string of Low Rank approaches and then the new set of algorithms
that provide robust PCA in light of sparse but potentially large
errors/outliers (typically the sort of outliers that break normal PCA).
These recent algorithms initially come from some of the folks  involved in
compressive sensing.

I am keeping a list of all these new solvers here in the Matrix
Factorization Jungle Page @

https://sites.google.com/site/igorcarron2/matrixfactorizations

Most are written in Matlab and should not need be too difficult to
translate into R.

To get a sense of what these new Robust PCA techniques can do, a friend and
I apply on different YouTube videos, you can see some of the entries listed
here:

http://nuit-blanche.blogspot.com/p/its-cai-cable-and-igors-adventures-in.html




------------------------
Igor Carron, Ph.D.
http://nuit-blanche.blogspot.com

	[[alternative HTML version deleted]]

