Alleviating Label Switching with Optimal Transport

Autor:	Monteiller, Pierre, Claici, Sebastian, Chien, Edward, Mirzazadeh, Farzaneh, Solomon, Justin, Yurochkin, Mikhail
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.02053 Zobrazit plný text záznamu View this record from Arxiv