论文标题
用于分层更改点检测的多项式采样
Multinomial Sampling for Hierarchical Change-Point Detection
论文作者
论文摘要
贝叶斯变更点检测以及潜在变量模型，可以对高维时序进行分割。我们假设变更点位于较低维的流形上，我们旨在推断离散潜在变量的子集。对于此模型，完整的推断在计算上是不可行的，并且使用基于点估计的伪观察。但是，如果估计不够确定，则更改点检测会受到影响。为了解决这个问题，我们提出了一种多项式抽样方法，该方法可以提高检测率并减少延迟，同时保持复杂性稳定且可以在分析上进行分析。我们的实验表明结果表现优于基线方法，我们还提供了一个针对人类行为研究的示例。
Bayesian change-point detection, together with latent variable models, allows to perform segmentation over high-dimensional time-series. We assume that change-points lie on a lower-dimensional manifold where we aim to infer subsets of discrete latent variables. For this model, full inference is computationally unfeasible and pseudo-observations based on point-estimates are used instead. However, if estimation is not certain enough, change-point detection gets affected. To circumvent this problem, we propose a multinomial sampling methodology that improves the detection rate and reduces the delay while keeping complexity stable and inference analytically tractable. Our experiments show results that outperform the baseline method and we also provide an example oriented to a human behavior study.
