Learning Shared Kernel Models: the Shared Kernel EM algorithm. (arXiv:2205.09041v1 [cs.LG])

Expectation maximisation (EM) is an unsupervised learning method for
estimating the parameters of a finite mixture distribution. It works by
introducing "hidden" or "latent" variables via Baum's auxiliary function $Q$
that allow the joint data likelihood to be expressed as a product of simple
factors. The relevance of EM has increased since the introduction of the
variational lower bound (VLB): the VLB differs from Baum's auxiliary function
only by the entropy of the PDF of the latent variables $Z$. We …

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