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We study Sinkhorn EM (sEM), a variant of the expectation maximization (EM) algorithm for mixtures based on entropic optimal transport.
Differentiable deep clustering with cluster size constraints
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Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
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Maximum likelihood from incomplete data via the EM algorithm
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Mixture densities, maximum likelihood and the EM algorithm
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Improved gaussian mixture density estimates using bayesian penalty terms and network averaging
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On convergence properties of the EM algorithm for gaussian mixtures
Xu, L. and Jordan, M. I. (1996) · 1996
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A view of the EM algorithm that justifies incremental, sparse, and other variants
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McLachlan, G. J. and Krishnan, T. (2008) · 2008
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The variational approximation for Bayesian inference
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Parameter estimation in finite mixture models by regularized optimal transport: A unified framework for hard and soft clustering
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Semidual regularized optimal transport
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Entropic optimal transport is maximum-likelihood deconvolution
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Benefits of over-parameterization with EM
Xu, J., Hsu, D. J., and Maleki, A. (2018) · 2018
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Computational optimal transport
Peyré, G., Cuturi, M., et al. (2019) · 2019
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