Transactions of the American Mathematical Society, Vol. 359, No. 12 (Dec., 2007), pp. 5725-5759 (35 pages) Let the space ℝⁿ be endowed with a Minkowski structure M (that is, M: ℝⁿ → [0, + ∞]) is the ...

What if instead of defining a mesh as a series of vertices and edges in a 3D space, you could describe it as a single function? The easiest function would return the signed distance to the closest ...

The Data Science Lab Wasserstein Distance Using C# and Python Dr. James McCaffrey of Microsoft Research shows how to compute the Wasserstein distance and explains why it is often preferable to ...

The field of metric learning focuses on tailoring distance metrics to better capture the underlying structure in data by learning from examples. By optimising the measure of similarity between samples ...

Many common distance functions do not meet one or more of these conditions. For example, Kullback-Leibler divergence meets only condition 1. Symmetric Kullback-Leibler distance meets conditions 1 and ...