Divergence-Based Motivation for Online EM and Combining Hidden Variable Models

arXiv: Learning, 2019.

Cited by: 4|Views17
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Abstract:

Expectation-Maximization (EM) is the fallback method for parameter estimation of hidden (aka latent) variable models. Given the full batch of data, EM forms an upper-bound of the negative log-likelihood of the model at each iteration and then updates to the minimizer of this upper-bound. We introduce a versatile online variant of EM whe...More

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