图 书 馆
基于期望传播的相关时间序列可扩展聚类Scalable Clustering of Correlated Time Series using Expectation Propagation |
|
| 课程网址: | https://videolectures.net/videos/kdd2016_aicher_expectation_propa... |
| 主讲教师: | Christopher Aicher |
| 开课单位: | KDD 2016研讨会 |
| 开课时间: | 2025-02-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We are interested in finding clusters of time series such that series within a cluster are correlated and series between clusters are independent. Existing Bayesian methods for inferring correlated clusters of time series either: (i) require conditioning on latent variables to decouple time series, but results in slow mixing or (ii) require calculating a collapsed likelihood, but with computation scaling cubically with the number of time series per cluster. To infer the latent cluster assignments efficiently, we consider approximate methods that trade exactness for scalability. Our main contribution is the development of an expectation propagation based approximation for the collapsed likelihood approach. Our empirical results on synthetic data show our methods scale linearly instead of cubically, while maintaining competitive accuracy. |
| 关 键 词: | 期望传播; 时间序列; 可扩展聚类 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-04-06：liyq |
| 最后编审: | 2025-04-06：liyq |
| 阅读次数: | 9 |