图 书 馆
聚类中的信息论模型选择Information Theoretic Model Selection in Clustering |
|
| 课程网址: | http://videolectures.net/nipsworkshops09_buhmann_itm/ |
| 主讲教师: | Joachim M. Buhmann |
| 开课单位: | 苏黎世联邦理工学院 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Model selection in clustering requires (i) to specify a clustering principle and (ii) to decide an appropriate number of clusters depending on the noise level in the data. We advocate an information theoretic perspective where the uncertainty in the data set induces an uncertainty in the solution space of clusterings. A clustering model, which can tolerate a higher level of noise in the data than competing models, is considered to be superior provided that the clustering solution is equally informative. This tradeoff between informativeness and robustness is used as a model selection criterion. The request that solutions should generalize from one data set to an equally probable second data set gives rise to a new notion of structure induced information. |
| 关 键 词: | 聚类原理; 噪声水平; 数据集 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-07：lxf |
| 阅读次数: | 42 |