图 书 馆
时空数据稀疏回归的非参数混合——在气候预测中的应用Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data -- An Application to Climate Prediction |
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| 课程网址: | http://videolectures.net/kdd2019_liu_chen_ganguly/ |
| 主讲教师: | Yumin Liu |
| 开课单位: | 东北大学 |
| 开课时间: | 2020-03-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Climate prediction is a very challenging problem. Many institutes around the world try to predict climate variables by building climate models called General Circulation Models (GCMs), which are based on mathematical equations that describe the physical processes. The prediction abilities of different GCMs may vary dramatically across different regions and time. Motivated by the need of identifying which GCMs are more useful for a particular region and time, we introduce a clustering model combining Dirichlet Process (DP) mixture of sparse linear regression with Markov Random Fields (MRFs). This model incorporates DP to automatically determine the number of clusters, imposes MRF constraints to guarantee spatio-temporal smoothness, and selects a subset of GCMs that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. Experimental results are provided for both synthetic and real-world climate data. |
| 关 键 词: | 时空数据稀疏回归; 非参数混合; 在气候预测中的应用 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-09-19：cyh |
| 最后编审: | 2022-09-19：cyh |
| 阅读次数: | 28 |