Dirichlet process mixture models for non-stationary data streams. (arXiv:2210.06872v1 [stat.ML])

In recent years, we have seen a handful of work on inference algorithms over
non-stationary data streams. Given their flexibility, Bayesian non-parametric
models are a good candidate for these scenarios. However, reliable streaming
inference under the concept drift phenomenon is still an open problem for these
models. In this work, we propose a variational inference algorithm for
Dirichlet process mixture models. Our proposal deals with the concept drift by
including an exponential forgetting over the prior global parameters. Our
algorithm …

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