  In this paper it is shown how to map a data manifold into a simpler form by
progressively discarding small degrees of freedom. This is the key to
self-organising data fusion, where the raw data is embedded in a very
high-dimensional space (e.g. the pixel values of one or more images), and the
requirement is to isolate the important degrees of freedom which lie on a
low-dimensional manifold. A useful advantage of the approach used in this paper
is that the computations are arranged as a feed-forward processing chain, where
all the details of the processing in each stage of the chain are learnt by
self-organisation. This approach is demonstrated using hierarchically
correlated data, which causes the processing chain to split the data into
separate processing channels, and then to progressively merge these channels
wherever they are correlated with each other. This is the key to
self-organising data fusion.
