Firstly, thanks for a fantastic program, I've used it extensively in my own research and have had great success.
To my problem, I'm trying to use the feature transformation kernelPCA. However it invariably gives me an error along the lines of:
P Jul 9, 2009 1:42:49 AM: ModelApplier: Applying com.rapidminer.operator.features.transformation.KernelPCAModel
P Jul 9, 2009 1:42:49 AM: KernelPCA: Adding new the derived features...
P Jul 9, 2009 1:42:49 AM: KernelPCA: Calculating new features
G Jul 9, 2009 1:42:49 AM: [Fatal] ArrayIndexOutOfBoundsException occured in 1st application of ModelApplier (ModelApplier)
G Jul 9, 2009 1:42:49 AM: [Fatal] Process failed: operator cannot be executed (60). Check the log messages...
+- ExampleSource (ExampleSource)
+- KernelPCA (KernelPCA)
here ==> +- ModelApplier (ModelApplier)
This example is relatively simple compared to my actual setup, but fails just the same. If I change this kernelPCA to a PCA or GHA for instance, it works just fine. Is kernelPCA really different to PCA in terms of RapidMiner setup? (obivously it is in effect, but I can worry about whether its a good idea if I can get it to work
However, each of these appears to take an ExampleSet, then return an ExampleSet and a Model, which implies kernelPCA also should allow a ModelApplier as GHA or PCA do.
Note that the index of the error (60 in the above) always appears to be 1 past the number of attributes I have in the ExampleSet.
I'm sure I'm missing something, but help would be much appreciated
All this is in RapidMiner 4.4 CE.