Entropic optimal transport yields a clustering loss with the same global optimum as log-likelihood but a better-behaved optimization surface, outperforming standard EM in experiments.
Journal of Multivariate Analysis , volume=
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The authors relax the closeness-to-identity condition in Assumption (B) of Gusakova et al. so that central and stable limit theorems for simplex volumes hold under spiked and Toeplitz covariance structures.
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On Model-Based Clustering With Entropic Optimal Transport
Entropic optimal transport yields a clustering loss with the same global optimum as log-likelihood but a better-behaved optimization surface, outperforming standard EM in experiments.
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A note on "The volume of random simplices from elliptical distributions in high dimension"
The authors relax the closeness-to-identity condition in Assumption (B) of Gusakova et al. so that central and stable limit theorems for simplex volumes hold under spiked and Toeplitz covariance structures.