论文标题
高斯图形模型作为分布式高斯过程的合奏方法
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes
论文作者
论文摘要
分布式高斯流程（DGP）是将GP扩展到大数据的流行方法，将培训数据划分为某些子集，对每个分区执行局部推断，并汇总结果以获取全球预测。为了结合局部预测，使用条件独立性假设，这基本上意味着亚集群之间存在完美的多样性。尽管它可以使聚合可进行处理，但在实践中通常会违反，并且通常会产生差的结果。在本文中，我们提出了一种新的方法，用于通过高斯图形模型（GGM）汇总高斯专家的预测，其中目标聚集被定义为未观察到的潜在变量，而局部预测是观察到的变量。我们首先使用预期最大化（EM）算法估算潜在和观察到的变量的联合分布。专家之间的相互作用可以通过关节分布的精确矩阵来编码，并且根据条件高斯分布的特性获得了汇总预测。使用合成数据集和实际数据集，我们的实验评估表明，我们的新方法的表现优于其他最先进的DGP方法。
Distributed Gaussian process (DGP) is a popular approach to scale GP to big data which divides the training data into some subsets, performs local inference for each partition, and aggregates the results to acquire global prediction. To combine the local predictions, the conditional independence assumption is used which basically means there is a perfect diversity between the subsets. Although it keeps the aggregation tractable, it is often violated in practice and generally yields poor results. In this paper, we propose a novel approach for aggregating the Gaussian experts' predictions by Gaussian graphical model (GGM) where the target aggregation is defined as an unobserved latent variable and the local predictions are the observed variables. We first estimate the joint distribution of latent and observed variables using the Expectation-Maximization (EM) algorithm. The interaction between experts can be encoded by the precision matrix of the joint distribution and the aggregated predictions are obtained based on the property of conditional Gaussian distribution. Using both synthetic and real datasets, our experimental evaluations illustrate that our new method outperforms other state-of-the-art DGP approaches.
