  In this paper a stochastic generalisation of the standard Linde-Buzo-Gray
(LBG) approach to vector quantiser (VQ) design is presented, in which the
encoder is implemented as the sampling of a vector of code indices from a
probability distribution derived from the input vector, and the decoder is
implemented as a superposition of reconstruction vectors, and the stochastic VQ
is optimised using a minimum mean Euclidean reconstruction distortion
criterion, as in the LBG case. Numerical simulations are used to demonstrate
how this leads to self-organisation of the stochastic VQ, where different
stochastically sampled code indices become associated with different input
subspaces. This property may be used to automate the process of splitting
high-dimensional input vectors into low-dimensional blocks before encoding
them.
