Gaussian Mixture Identifiability from degree 6 Moments

We resolve most cases of identifiability from sixth-order moments for Gaussian mixtures on spaces of large dimensions. Our results imply that the parameters of a generic mixture of m≤𝒪(n^4) Gaussians on ℝ^n can be uniquely recovered from the mixture moments of degree 6. The constant hidden in the 𝒪-notation is optimal and equals the one in the upper bound from counting parameters. We give an argument that degree-4 moments never suffice in any nontrivial case, and we conduct some numerical experiments indicating that degree 5 is minimal for identifiability.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/28/2019

The multidimensional truncated Moment Problem: Shape and Gaussian Mixture Reconstruction from Derivatives of Moments

In this paper we introduce the theory of derivatives of moments and (mom...
research
05/13/2019

Moment Identifiability of Homoscedastic Gaussian Mixtures

We consider the problem of identifying a mixture of Gaussian distributio...
research
02/08/2021

Learning Diagonal Gaussian Mixture Models and Incomplete Tensor Decompositions

This paper studies how to learn parameters in diagonal Gaussian mixture ...
research
12/09/2016

A series of maximum entropy upper bounds of the differential entropy

We present a series of closed-form maximum entropy upper bounds for the ...
research
05/04/2020

How many modes can a constrained Gaussian mixture have?

We show, by an explicit construction, that a mixture of univariate Gauss...
research
10/04/2018

Gaussian approximation of Gaussian scale mixture

For a given positive random variable V>0 and a given Z∼ N(0,1) independe...

Please sign up or login with your details

Forgot password? Click here to reset