论文标题
检测潜在变量模型的层次变化
Detecting Hierarchical Changes in Latent Variable Models
论文作者
论文摘要
本文解决了从数据流中检测潜在变量模型（HCDL）层次变化的问题。潜在变量模型有三个不同级别的更改：1）第一个级别是固定潜在变量的数据分布的变化，2）第二个级别是，第二个级别是潜在变量的分布中，3）第三个是潜在变量的数量。检测这些变化很重要，因为我们可以通过识别哪个更改来自（可解释性）来分析变化的原因。本文提出了一个信息理论框架，用于以层次结构的方式检测三个级别的变化。意识到它的关键思想是使用MDL（最小描述长度）更改统计数据来测量变化程度，并结合DNML（分解归一化的最大可能性）代码长度计算。我们为更改提供可靠的警报提供了理论基础。为了关注随机块模型，我们采用合成和基准数据集以经验证明我们框架在变化可解释性和变化检测方面的有效性。
This paper addresses the issue of detecting hierarchical changes in latent variable models (HCDL) from data streams. There are three different levels of changes for latent variable models: 1) the first level is the change in data distribution for fixed latent variables, 2) the second one is that in the distribution over latent variables, and 3) the third one is that in the number of latent variables. It is important to detect these changes because we can analyze the causes of changes by identifying which level a change comes from (change interpretability). This paper proposes an information-theoretic framework for detecting changes of the three levels in a hierarchical way. The key idea to realize it is to employ the MDL (minimum description length) change statistics for measuring the degree of change, in combination with DNML (decomposed normalized maximum likelihood) code-length calculation. We give a theoretical basis for making reliable alarms for changes. Focusing on stochastic block models, we employ synthetic and benchmark datasets to empirically demonstrate the effectiveness of our framework in terms of change interpretability as well as change detection.
