图 书 馆
希尔伯特施密特的依赖最大化的方法来监督结构发现A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery |
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| 课程网址: | http://videolectures.net/mlg08_gretton_ahsdma/ |
| 主讲教师: | Arthur Gretton |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2008-08-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In recent work by (Song et al., 2007), it has been proposed to perform clustering by maximizing a Hilbert-Schmidt independence criterion with respect to a predefined cluster structure Y, by solving for the partition matrix. We extend this approach here to the case where the cluster structure Y is not fixed, but is a quantity to be optimized and we use an independence criterion which has been shown to be more sensitive at small sample sizes (the Hilbert-Schmidt Normalized Information Criterion, or HSNIC (Fukumizu et al., 2008)). We demonstrate the use of this framework in two scenarios. In the first, we adopt a cluster structure selection approach in which the HSNIC is used to select a structure from several candidates. In the second, we consider the case where we discover structure by directly optimizing Y. |
| 关 键 词: | 聚类与预定义; 集群结构最大化; 分区矩阵 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-10-22：chenxin |
| 阅读次数: | 61 |