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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Online Regression for Data With Changepoints Using Gaussian Processes and Reusable Models
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Online Regression for Data With Changepoints Using Gaussian Processes and Reusable Models

机译：使用高斯过程和可重用模型对具有变更点的数据进行在线回归

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摘要

Many prediction, decision-making, and control architectures rely on online learned Gaussian process (GP) models. However, most existing GP regression algorithms assume a single generative model, leading to poor predictive performance when the data are nonstationary, i.e., generated from multiple switching processes. Furthermore, existing methods for GP regression over nonstationary data require significant computation, do not come with provable guarantees on correctness and speed, and many only work in batch settings, making them ill-suited for real-time prediction. We present an efficient online GP framework, GP-non-Bayesian clustering (GP-NBC), which addresses these computational and theoretical issues, allowing for real-time changepoint detection and regression using GPs. Our empirical results on two real-world data sets and two synthetic data set show that GP-NBC outperforms state-of-the-art methods for nonstationary regression in terms of both regression error and computation. For example, it outperforms Dirichlet process GP clustering with Gibbs sampling by 98% in computation time reduction while the mean absolute error is comparable.
机译：许多预测，决策和控制体系结构都依赖于在线学习的高斯过程（GP）模型。但是，大多数现有的GP回归算法都采用单一的生成模型，当数据不稳定（即由多个转换过程生成）时，导致较差的预测性能。此外，用于对非平稳数据进行GP回归的现有方法需要大量计算，没有提供正确性和速度的可证明保证，并且许多方法只能在批处理设置中工作，这使其不适用于实时预测。我们提出了一种有效的在线GP框架，即GP-非贝叶斯聚类（GP-NBC），它解决了这些计算和理论问题，允许使用GP进行实时变化点检测和回归。我们在两个实际数据集和两个综合数据集上的经验结果表明，就回归误差和计算而言，GP-NBC优于非平稳回归的最新方法。例如，它的Gibbs采样比Dirichlet过程GP聚类的性能要好98％，而平均绝对误差是可比的。

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