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Poster

A Randomized Algorithm to Reduce the Support of Discrete Measures

Francesco Cosentino · Harald Oberhauser · Alessandro Abate

Poster Session 3 #1018

Abstract: Given a discrete probability measure supported on N atoms and a set of n real-valued functions, there exists a probability measure that is supported on a subset of n+1 of the original N atoms and has the same mean when integrated against each of the n functions. If Nn this results in a huge reduction of complexity. We give a simple geometric characterization of barycenters via negative cones and derive a randomized algorithm that computes this new measure by greedy geometric sampling''. We then study its properties, and benchmark it on synthetic and real-world data to show that it can be very beneficial in the Nn regime. A Python implementation is available at \url{https://github.com/FraCose/Recombination_Random_Algos}.

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