Alleviating label switching with optimal transport

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Alleviating label switching with optimal transport

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NeurIPS-2019-alleviating-label-switching-with-optimal-transport-Paper.pdf

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Published version

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Date Issued

December 2019

Journal

Advances in Neural Information Processing Systems

Citation

2019. "Alleviating label switching with optimal transport." Advances in Neural Information Processing Systems, 32.

Version

Final published version

Abstract

© 2019 Neural information processing systems foundation. All rights reserved. Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedures. This issue arises due to invariance of the posterior under actions of a group; for example, permuting the ordering of mixture components has no effect on the likelihood. We propose a resolution to label switching that leverages machinery from optimal transport. Our algorithm efficiently computes posterior statistics in the quotient space of the symmetry group. We give conditions under which there is a meaningful solution to label switching and demonstrate advantages over alternative approaches on simulated and real data.

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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.

Persistent DSpace Link

https://hdl.handle.net/1721.1/137353

DOI of Published Version

https://papers.nips.cc/paper/2019/hash/c2ae5cb2426d96ed19a50b0b7d7c8e11-Abstract.html