  We propose a solution to the problem of estimating a Riemannian metric
associated with a given differentiable manifold. The metric learning problem is
based on minimizing the relative volume of a given set of points. We derive the
details for a family of metrics on the multinomial simplex. The resulting
metric has applications in text classification and bears some similarity to
TFIDF representation of text documents.
