An Improved Analysis of the Quadtree for High Dimensional EMD
December 2, 2020 (Zoom - See email or contact organizers for link)


The Earth Mover Distance (EMD) between two multi-sets A,B in R^d of size s is the min-cost of bipartite matchings between points in A and B, where cost is measured by distance between points. In this talk, we discuss a classic divide-and-conquer algorithm known as Quadtree for approximating EMD.

We give a new analysis of the Quadtree, showing that it gives a Õ(log s) approximation. This improves on the previous known O(min{log s , log d} * log s)-approximation of Andoni, Indyk, and Krauthgamer (SODA 08), and Backurs, Dong, Indyk, Razenshteyn, and Wagner (ICML 20).

We also give new space efficient sketching and streaming algorithms for estimating EMD with the improved approximation factor. The main conceptual contribution is an analytical framework for studying the Quadtree which goes beyond worst-case distortion of randomized tree embeddings.