Memoized Online Variational Inference for Dirichlet Process Mixture Models

  title={Memoized Online Variational Inference for Dirichlet Process Mixture Models},
  author={Michael C. Hughes and Erik B. Sudderth},
Variational inference algorithms provide the most effective framework for largescale training of Bayesian nonparametric models. Stochastic online approaches are promising, but are sensitive to the chosen learning rate and often converge to poor local optima. We present a new algorithm, memoized online variational inference, which scales to very large (yet finite) datasets while avoiding the complexities of stochastic gradient. Our algorithm maintains finite-dimensional sufficient statistics… CONTINUE READING
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