Date: Apr 10, 2013 11:44 PM
Author: Hosley
Subject: Principal Component Analysis Alternatives for low sample to<br> dimensions ratio

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.

Thanks,
Krevin