We present a method of generating Mercer Kernels
from an ensemble of probabilistic mixture models,
where each mixture model is generated from a Bayesian
mixture density estimate. We show how to convert the
ensemble estimates into a Mercer Kernel, describe the
properties of this new kernel function, and give examples
of the performance of this kernel on unsupervised
clustering of synthetic data and also in the domain of
unsupervised multispectral image understanding.