An Efficient 1 Iteration Learning Algorithm for Gaussian Mixture Model And Gaussian Mixture Embedding For Neural Network

TL;DR

This paper introduces a fast, robust Gaussian Mixture Model learning algorithm that converges in one iteration, improving accuracy and uncertainty handling in neural network applications.

Contribution

The paper presents a novel one-iteration GMM learning algorithm with theoretical convergence guarantees, outperforming classic EM in robustness and accuracy.

Findings

The new algorithm converges in a single iteration.

It demonstrates improved robustness over EM.

GMM-based neural network layers better handle data uncertainty.

Abstract

We propose an Gaussian Mixture Model (GMM) learning algorithm, based on our previous work of GMM expansion idea. The new algorithm brings more robustness and simplicity than classic Expectation Maximization (EM) algorithm. It also improves the accuracy and only take 1 iteration for learning. We theoretically proof that this new algorithm is guarantee to converge regardless the parameters initialisation. We compare our GMM expansion method with classic probability layers in neural network leads to demonstrably better capability to overcome data uncertainty and inverse problem. Finally, we test GMM based generator which shows a potential to build further application that able to utilized distribution random sampling for stochastic variation as well as variation control.