Dirichlet process mixture models for non-stationary data streams

TL;DR

This paper introduces a variational inference algorithm for Dirichlet process mixture models that adapt to concept drift in non-stationary data streams by incorporating exponential forgetting, improving clustering and density estimation.

Contribution

It presents a novel variational inference method with exponential forgetting for Dirichlet process mixtures, enabling automatic adaptation to concept drift in data streams.

Findings

Competitive in density estimation tasks.

Outperforms existing methods in clustering.

Effective handling of concept drift through exponential forgetting.

Abstract

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.