
We present new deterministic methods that given two eigenspace
models  each representing a set of ndimensional observations will:
 merge
the models to yield a representation of the union of the sets and
 split one model
from another to represent the difference between the sets.
As this is done, we
accurately keep track of the mean. Here, we give a theoretical derivation of the
methods, empirical results relating to the efficiency and accuracy of the
techniques, and three general applications, including the construction of Gaussian
mixture models that are dynamically updateable.
