questions on kohonen's maps

[ananth sankar]

I am interested in the subject of Self Organization and have some
questions with regard to Kohonen's algorithm for Self Organizing Feature
Maps. I have tried to duplicate the results of Kohonen for the two dimensional
uniform input case i.e. two inputs. I used a 10 X 10 output grid. The maps
that resulted were not as good as reported in the papers.

Questions:
	
	
1	Is there any analytical expression for the neighbourhood and gain
	functions? I have seen a simulation were careful tweaking after
	every so many iterations produces a correctly evolving map. This
	is obviously not a proper approach.

2	Even if good results are available for particular functions for
	the uniform distribution input case, it is not clear to me that these
	same functions would result in good classification for some other
	problem. I have attempted to use these maps for word classification
	using LPC coeffs as features.

3	In Kohonen's book "Self Organization and Associative Memory", Ch 5
	the algorithm for weight adaptation does not produce normalized
	weights. Thus the output nodes cannot function as simply as taking
	a dot product of inputs and weights. They have to execute a distance
	calculation.

4	I have not seen as yet in the literature any reports on
	how the fact that neighbouring nodes respond to similar patterns
	from a feature space can be exploited.

5	Can the net become disordered after ordering is achieved at any
	particular iteration? 


I would appreciate any comments, suggestions etc on the above. Also so that
net mail clutter may be reduced please respond to

sankar at caip.rutgers.edu

Thank you.

Ananth Sankar
Department of Electrical Engineering
Rutgers University, NJ