  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.
