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]] ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
