Dirichlet process mixture models for non-stationary data streams
This addresses the open problem of reliable streaming inference under concept drift for Bayesian non-parametric models, which is incremental as it builds on existing work with a specific algorithmic improvement.
The authors tackled the problem of concept drift in non-stationary data streams by proposing a variational inference algorithm for Dirichlet process mixture models with exponential forgetting, resulting in competitive density estimation and outperforming state-of-the-art algorithms in clustering.
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 allows to adapt the learned model to the concept drifts automatically. We perform experiments in both synthetic and real data, showing that the proposed model is competitive with the state-of-the-art algorithms in the density estimation problem, and it outperforms them in the clustering problem.