  This report concerns the problem of dimensionality reduction through
information geometric methods on statistical manifolds. While there has been
considerable work recently presented regarding dimensionality reduction for the
purposes of learning tasks such as classification, clustering, and
visualization, these methods have focused primarily on Riemannian manifolds in
Euclidean space. While sufficient for many applications, there are many
high-dimensional signals which have no straightforward and meaningful Euclidean
representation. In these cases, signals may be more appropriately represented
as a realization of some distribution lying on a statistical manifold, or a
manifold of probability density functions (PDFs). We present a framework for
dimensionality reduction that uses information geometry for both statistical
manifold reconstruction as well as dimensionality reduction in the data domain.
