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首页> 外文会议>International conference on neural information processing;Annual conference of Asia-Pacific Neural Network Society >Exploring Latent Structure Similarity for Bayesian Nonparameteric Model with Mixture of NHPP Sequence
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Exploring Latent Structure Similarity for Bayesian Nonparameteric Model with Mixture of NHPP Sequence

机译：探索具有NHPP序列混合的贝叶斯非参数模型的潜在结构相似性

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摘要

Temporal point process data has been widely observed in many applications including finance, health, and infrastructures, so that it has become an important topic in data analytics domain. Generally, a point process only records occurrence of a type of event as 1 or 0. To interpret the temporal point process, it is important to estimate the intensity of the occurrence of events, which is challenging especially when the intensity is dynamic over time, for example non-homogeneous Poisson process (NHPP) which is exactly what we will analyse in this paper. We performed a joint task to determine which two NHPP sequences are in the same group and to estimate the intensity resides in that group. Distance dependent Chinese Restaurant Process (ddCRP) provides a prior to cluster data points within a Bayesian nonparametric framework, alleviating the required knowledge to set the number of clusters which is sensitive in clustering problems. However, the distance in previous studies of ddCRP is designed for data points, in this paper such distance is measured by dynamic time warping (DTW) due to its wide application in ordinary time series (e.g. observed values are in TV). The empirical study using synthetic and real-world datasets shows promising outcome compared with the alternative techniques.
机译：时间点过程数据已在包括财务，健康和基础结构在内的许多应用程序中得到广泛观察，因此它已成为数据分析领域中的重要主题。通常，点过程仅将事件类型的发生记录为1或0。为了解释时间点过程，估算事件发生的强度非常重要，这尤其具有挑战性，因为强度随时间变化是动态的，例如非均匀泊松过程（NHPP），这正是我们将在本文中分析的内容。我们执行了一项联合任务，以确定哪个NHPP序列在同一组中，并估计强度在该组中。距离相关的中国餐馆过程（ddCRP）在贝叶斯非参数框架内提供了先于集群的数据点，从而减轻了设置集群数量敏感的集群数量所需的知识。但是，以前ddCRP研究中的距离是为数据点设计的，由于其在普通时间序列中的广泛应用（例如，观测值在电视中），本文通过动态时间规整（DTW）来测量该距离。使用合成数据和真实数据集进行的实证研究表明，与替代技术相比，结果令人鼓舞。

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