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Topic: Principal Component Analysis Alternatives for low sample to
dimensions ratio

Replies: 5   Last Post: Apr 11, 2013 11:26 PM

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Richard Ulrich

Posts: 2,961
Registered: 12/13/04
Re: Principal Component Analysis Alternatives for low sample to dimensions ratio
Posted: Apr 11, 2013 2:36 PM
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On Wed, 10 Apr 2013 20:44:39 -0700 (PDT), Krevin <>

>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.

Rich Ulrich

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