A note on the relations between mixture models, maximum-likelihood and entropic optimal transport
Year: 2025
Type: preprint
Abstract: This note aims to demonstrate that performing maximum-likelihood estimation for a mixture model is equivalent to minimizing over the parameters an optimal transport problem with entropic regularization. The objective is pedagogical: we seek to present this already known result in a concise and hopefully simple manner. We give an illustration with Gaussian mixture models by showing that the standard EM algorithm is a specific block-coordinate descent on an optimal transport loss.
Source: arXiv (Cornell University)
Authors: Vayer, Titouan, Lasalle, Etienne
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6891996952