A kernel mean embedding-based Gaussian mixture framework is introduced for clustering Hilbert space-valued data with proofs of well-defined algorithms and dense approximations.
An Analysis of Distributional Reinforcement Learning with Gaussian Mixtures
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Gaussian mixture models in Hilbert spaces via kernel methods
A kernel mean embedding-based Gaussian mixture framework is introduced for clustering Hilbert space-valued data with proofs of well-defined algorithms and dense approximations.