Paper in neuroprose

[Thomas H. Hildebrandt]

   Date: Wed, 18 Mar 92 11:35:13 -0800
   From: garyc at cs.uoregon.edu


   But your measure of redundancy - collinearity - seems appropriate for
   your linear domain; what about redundancy for a nonlinear map?


   gary cottrell

I think that the appropriate measure is the degree of collinearity of
the training vectors in class space, i.e. after the nonlinear mapping
has been performed.  Obviously, this requires you to know the answer
(i.e. have in hand the completely trained network) before you can
measure redundancy, so the measure is not very useful.
	However, if you accept it as the correct definition of
redundancy, then you can apply certain assumptions (e.g. local
linearity of the input space, linearity in certain subspaces, etc.)
which will allow you to estimate the measure a priori with varying
degrees of accuracy.
				Thomas H. Hildebrandt
				CSEE Department
				Lehigh University