论文标题
非平稳数据流的Dirichlet工艺混合模型
Dirichlet process mixture models for non-stationary data streams
论文作者
论文摘要
近年来，我们在非平稳数据流上看到了一些关于推论算法的工作。鉴于它们的灵活性，贝叶斯非参数模型是这些情况的好候选人。但是，对于这些模型而言，概念漂移现象下的可靠流推断仍然是一个空旷的问题。在这项工作中，我们为Dirichlet工艺混合模型提出了一种变异推理算法。我们的建议涉及概念漂移，包括对先前的全局参数的指数遗忘。我们的算法允许将学习的模型适应自动漂移的概念。我们在合成数据和实际数据中执行实验，表明所提出的模型在密度估计问题中与最新的算法具有竞争力，并且在聚类问题中表现出色。
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.
