  The processing of mega-dimensional data, such as images, scales linearly with
image size only if fixed size processing windows are used. It would be very
useful to be able to automate the process of sizing and interconnecting the
processing windows. A stochastic encoder that is an extension of the standard
Linde-Buzo-Gray vector quantiser, called a stochastic vector quantiser (SVQ),
includes this required behaviour amongst its emergent properties, because it
automatically splits the input space into statistically independent subspaces,
which it then separately encodes. Various optimal SVQs have been obtained, both
analytically and numerically. Analytic solutions which demonstrate how the
input space is split into independent subspaces may be obtained when an SVQ is
used to encode data that lives on a 2-torus (e.g. the superposition of a pair
of uncorrelated sinusoids). Many numerical solutions have also been obtained,
using both SVQs and chains of linked SVQs: (1) images of multiple independent
targets (encoders for single targets emerge), (2) images of multiple correlated
targets (various types of encoder for single and multiple targets emerge), (3)
superpositions of various waveforms (encoders for the separate waveforms emerge
- this is a type of independent component analysis (ICA)), (4) maternal and
foetal ECGs (another example of ICA), (5) images of textures (orientation maps
and dominance stripes emerge). Overall, SVQs exhibit a rich variety of
self-organising behaviour, which effectively discovers the internal structure
of the training data. This should have an immediate impact on "intelligent"
computation, because it reduces the need for expert human intervention in the
design of data processing algorithms.
