missing values

[Martin Cooke]

I've only just seen the discussion on missing values, so forgive this late  
response. The issue of training the Kohonen self-organising feature map with  
partial data is covered in

	Samad & Harp (1992)
	Self-organisation with partial data
	Network, 3, 205-212.

Essentially, weight changes are restricted to the subspace of available data.  
Samad & Harp report three experiments using partial training data, and  
demonstrate that performance is essentially unchanged up to about 60% missing  
data. This is presumably due to the n -> 2 dimensionality reduction.

We recently applied this result to training a speech recogniser on partial  
data, and got similar results [tech. rep. in preparation]. We're coming at this  
from the field of auditory scene analysis, where the result of source  
segregation is an inherently partial description of one or other source.

I'd be happy to supply further details on request.

Martin Cooke
Computer Science
Sheffield University
UK