An Efficient 1 Iteration Learning Algorithm for Gaussian Mixture Model And Gaussian Mixture Embedding For Neural Network
This work presents an incremental improvement for researchers and practitioners using GMMs, offering faster convergence and better handling of data uncertainty in applications like neural networks and generative modeling.
The authors tackled the problem of learning Gaussian Mixture Models (GMMs) by proposing a new algorithm based on GMM expansion that requires only 1 iteration for learning, improving accuracy and robustness compared to the classic Expectation Maximization (EM) algorithm.
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.