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We address the problem of defining a network graph on a large collection ofclasses. Each class is comprised of a collection of data points, sampled in anon i.i.d. way, from some unknown underlying distribution. The application weconsider in this paper is a large scale high dimensional survey of peopleliving in the US, and the question of how similar or different are the variouscounties in which these people live. We use a co-clustering diffusion metric tolearn the underlying distribution of people, and build an approximate earthmover's distance algorithm using this data adaptive transportation cost.

People Mover's Distance: Class level geometry using fast pairwise data adaptive transportation costs