图 书 馆
用于多关系分类数据的无限混合Infinite mixtures for multi-relational categorical data |
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| 课程网址: | http://videolectures.net/mlg08_sinkkonen_im/ |
| 主讲教师: | Janne Sinkkonen |
| 开课单位: | 阿尔托大学 |
| 开课时间: | 2008-08-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Large relational datasets are prevalent in many fields. We propose an unsupervised component model for relational data, i.e., for heterogeneous collections of categorical co-occurrences. The co-occurrences can be dyadic or n-adic, and over the same or different categorical variables. Graphs are a special case, as collections of dyadic co occurrences (edges) over a set of vertices. The model is simple, with only one latent variable. This allows wide applicability as long as a global latent component solution is preferred, and the generative process fits the application. Estimation with a collapsed Gibbs sampler is straightforward. We demonstrate the model with graphs enriched with multinomial vertex properties, or more concretely, with two sets of scientific papers, with both content and citation information available. |
| 关 键 词: | 大型关系数据集; 无监督组件模型; 潜在变量 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-02：cjy |
| 阅读次数: | 43 |