>Anyone know of good alternative methods to PCA when you have too many dimensions compared to samples? > >If I have 2000 variables and 300 samples, I cannot properly use PCA. > >I'm looking for something that can minimize false positive separation of sample points without needing to reduce my number of variables.
As Art implies, it is usual for there to be some structure when there are as many as 2000 variables. The sort of structure is apt to matter for a constructive solution, assuming there is structure.
I don't know what you have in mind when you say, "minimize false separation of sample points..." but if you flip the matrix, you have another conventional factoring (of samples). I think of that as a sort of cluster analysis. Anyway, the samples with low communalities will be ones that are relatively "separate" from the others.