Alleviating Label Switching with Optimal Transport
Abstract
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.
Cite
@article{arxiv.1911.02053,
title = {Alleviating Label Switching with Optimal Transport},
author = {Pierre Monteiller and Sebastian Claici and Edward Chien and Farzaneh Mirzazadeh and Justin Solomon and Mikhail Yurochkin},
journal= {arXiv preprint arXiv:1911.02053},
year = {2019}
}
Comments
33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada