[pymvpa] Q about IterativeReliefOnline and more.......

[patrik andersson]

Hi,

I have a couple of questions regarding feature selection;

1. What choices are there in incremental feature selection and
training algorithms? Ideally, combined training and feature selection.

2. I was playing around with IterativeReliefOnline and got a bit
confused.  Lets say I have;

FeatureSelector = SensitivityBasedFeatureSelection(
               		IterativeReliefOnline(transformer=N.abs),
               		FixedNElementTailSelector(10000, mode='select',tail='upper'),
			enable_states = ['selected_ids'])
smap = FeatureSelector(dataset)

Is 'smap' now the values in 'dataset' applying the final set of
selected features on all samples?
The reason I ask is that, as far as I understand, the online version
of 'IterativeRelief' would get updated with one more sample at a time.
Can I access the feature selections from the previous states, when
only dataset[1:-k] "was available"?

Thanks a bunch for any help you can give me!

Cheers,
Patrik