We investigate the link between regularised self-transport problems and maximum likelihood estimation in Gaussian mixture models (GMM). This link suggests that self-transport followed by a clustering technique leads to principled estimators at a reasonable computational cost. Also, robustness, sparsity and stability properties of the optimal transport plan arguably make the regularised self-transport a statistical tool of choice for the GMM.

翻译：我们研究了正则化自传输问题与高斯混合模型（GMM）中最大似然估计之间的联系。这种联系表明，自传输结合聚类技术能够以合理的计算成本得到有原则的估计量。此外，最优传输规划的鲁棒性、稀疏性和稳定性特性，使正则化自传输成为GMM统计工具中的优选方案。