# Learning rates for Gaussian mixtures under group invariance

## TL;DR

This paper analyzes the estimation rates for Gaussian mixture models invariant under isometry groups, revealing a decomposition into components estimated at different rates and providing a geometric understanding of these rates.

## Contribution

It characterizes the maximum likelihood estimation rates for invariant Gaussian mixtures, including a detailed geometric and algebraic analysis of the likelihood landscape.

## Key findings

- Parameter decomposes into two components with different estimation rates
- Fast rate component estimated at n^{-1/2}
- Slow rate component estimated at n^{-1/4}

## Abstract

We study the pointwise maximum likelihood estimation rates for a class of Gaussian mixtures that are invariant under the action of some isometry group. This model is also known as multi-reference alignment, where random isometries of a given vector are observed, up to Gaussian noise. We completely characterize the speed of the maximum likelihood estimator, by giving a comprehensive description of the likelihood geometry of the model. We show that the unknown parameter can always be decomposed into two components, one of which can be estimated at the fast rate $n^{-1/2}$, the other one being estimated at the slower rate $n^{-1/4}$. We provide an algebraic description and a geometric interpretation of these facts.