Soft and subspace robust multivariate rank tests based on entropy regularized optimal transport release_y3jkv4rxjrchjoeul7lstzuvum

by Shoaib Bin Masud, Boyang Lyu, Shuchin Aeron

Released as a article .

2021  

Abstract

In this paper, we extend the recently proposed multivariate rank energy distance, based on the theory of optimal transport, for statistical testing of distributional similarity, to soft rank energy distance. Being differentiable, this in turn allows us to extend the rank energy to a subspace robust rank energy distance, dubbed Projected soft-Rank Energy distance, which can be computed via optimization over the Stiefel manifold. We show via experiments that using projected soft rank energy one can trade-off the detection power vs the false alarm via projections onto an appropriately selected low dimensional subspace. We also show the utility of the proposed tests on unsupervised change point detection in multivariate time series data. All codes are publicly available at the link provided in the experiment section.
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Type  article
Stage   submitted
Date   2021-03-16
Version   v1
Language   en ?
arXiv  2103.08811v1
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