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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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David Jones

Posts: 74
Registered: 2/9/12
Re: Principal Component Analysis Alternatives for low sample to dimensions ratio
Posted: Apr 11, 2013 12:48 PM
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"Krevin" wrote in message

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.



There are many possibilities, ranging between:
(1) A version of PCA in which you use a fictitious covariance matrix, not
estimated from the data but guessed from experience; a version of this might
estimate part of the covariance matrix with the rest filled in by assuming
zero correlations or partial correlations.
(2) A version of cluster analysis in which you define distances in the
variable space on the basis of relative importance on an intuitive scale; a
version of this might just use a weighted sum of squares with weights
derived from the sample variances, adjusted for any perceived overlaps in
meaning. But the idea here would be to have a good vision of "importance" of
the variables, with the sample statistics being not really relevant to the

David Jones

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