Computing the volume element of a family of metrics on the multinomial simplex
We compute the differential volume element of a family of metrics on the multinomial simplex. The metric family is composed of pull-backs of the Fisher information metric through a continuous group of transformations. This note complements the paper by Lebanon [3] that describes a metric learning framework and applies the results below to text classification.