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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: 62
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
news:910037f7-6687-4714-b75b-4a02268f1b8e@googlegroups.com...

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

=================================================================================

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
clustering.

David Jones




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