Weka's PCA is taking too long to run

Question

I am trying to use Weka for feature selection using PCA algorithm.

My original feature space contains ~9000 attributes, in 2700 samples.
I tried to reduce dimensiona

Answer 1

It looks like you're using the default configuration for the PCA, which judging by the long runtime, it is likely that it is doing way too much work for your purposes.

Take a look at the options for PrincipalComponents.

1. I'm not sure if -D means they will normalize it for you or if you have to do it yourself. You want your data to be normalized (centered about the mean) though, so I would do this yourself manually first.
2. -R sets the amount of variance you want accounted for. Default is 0.95. The correlation in your data might not be good so try setting it lower to something like 0.8.
3. -A sets the maximum number of attributes to include. I presume the default is all of them. Again, you should try setting it to something lower.

I suggest first starting out with very lax settings (e.g. -R=0.1 and -A=2) then working your way up to acceptable results.