ok... why is PCA different for regression and classification objects

[samkeene]

consider:

data = rand(30,1000); ds = prtDataSetRegress(data)

pca = prtPreProcPca('nComponents',100); pca = pca.train(ds);

%%

ds1 = prtDataGenFeatureSelection pca = prtPreProcPca('nComponents',100); pca = pca.train(ds1);

They both warn, but the first one tells me my data dimension is 30, the second warning say 8. But the regess object, my number of features is 1000, and num samples is 30. The second, my number of features is 8 and my num samples is 800. So the 1st one should not warn, no? What is the difference here because its a regression object?

[peterTorrione]

Ah.

This has nothing to do with regress or class objects.

The max number of PCA components is always bounded by the min(size(x)). (Which is usually # features, but for 30x1000 is num Observations).

I clarified the warning text to not say "#features", to instead say min(size(x)) = %d

Should clear it up