The transfer can be accomplished in six steps. Figure 2: Wasserstein Calculation Example. However, the symmetric Kullback-Leibler distance between (P, Q1) and the distance between (P, Q2) are both 1.79 - which doesn't make much sense. The Wasserstein distance between (P, Q1) = 1.00 and Wasserstein(P, Q2) = 2.00 - which is reasonable. The graphs of the three distributions, and common sense, tell you that P is closer to Q1 than it is to Q2. This article shows you how to compute the Wasserstein distance and explains why it is often preferable to alternative distance functions.Ī good way to see where this article is headed is to take a look at the screenshot of a demo program in Figure 1. Some of the most commonly used distance functions are Kullback-Leibler divergence, symmetric Kullback-Leibler distance, Jensen-Shannon distance, and Hellinger distance.
There are many different ways to measure the distance between two probability distributions.
What is the distance between P and Q? The distance between two distributions can be used in several ways, including measuring the difference between two images, comparing a data sample to the population from which the sample was drawn, and measuring loss/error for distribution-based neural systems such as variational autoencoders (VAEs). A common task in machine learning is measuring the distance between two probability distributions.
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