Wasserstein Distance Optimal Transport
Tags: #machine learning #wassersteinEquation
$$W_{p}(P,Q)=(\inf_{J \in J(P,Q)} \int{||x-y||^{p}dJ(X,Y)})^\frac{1}{p}$$Latex Code
W_{p}(P,Q)=(\inf_{J \in J(P,Q)} \int{||x-y||^{p}dJ(X,Y)})^\frac{1}{p}
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Introduction
Equation
Latex Code
W_{p}(P,Q)=(\inf_{J \in J(P,Q)} \int{||x-y||^{p}dJ(X,Y)})^\frac{1}{p}
Explanation
Latex code for the Wasserstein Distance (Optimal Transport Distance). I will briefly introduce the notations in this formulation.
: Wasserstein distance p power between two distributions P and Q
: Power p of distance moving distributions P towards Q
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