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.

翻译：我们提出了一种基于先前工作中高斯混合模型（GMM）扩展思想的学习算法。该新算法相比经典的期望最大化（EM）算法具有更强的鲁棒性和更简单的操作，同时仅需单次迭代即可完成学习，并显著提升了精度。我们从理论上证明了该新算法无论参数初始化如何均能保证收敛。通过将我们的GMM扩展方法与神经网络中的经典概率层进行对比，展示了其更优的应对数据不确定性和逆问题的能力。最后，我们对基于GMM的生成器进行了测试，结果表明该方法具备构建利用分布随机采样实现随机变异及变异控制的进一步应用潜力。