Two-sample Test using Projected Wasserstein Distance

arXiv:2010.11970v4 Announce Type: replace-cross
Abstract: We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they are from the same distribution. In particular, we aim to circumvent the curse of dimensionality in Wasserstein distance: when the dimension is high, it has diminishing testing power, which is inherently due to the slow concentration property of Wasserstein metrics in the high dimension space. A key contribution …

abstract aim arxiv cs.lg dimensionality distribution machine machine learning sample samples statistics stat.ml test the curse of dimensionality type