图 书 馆
狄利克雷聚合：无监督学习的比例数据的最佳度量Dirichlet Aggregation: Unsupervised Learning towards an Optimal Metric for Proportional Data |
|
| 课程网址: | http://videolectures.net/icml07_wang_daul/ |
| 主讲教师: | Hua-Yan Wang |
| 开课单位: | 北京大学 |
| 开课时间: | 2007-06-23 |
| 课程语种: | 汉简 |
| 中文简介: |  |
| 课程简介: | Proportional data (normalized histograms) have been frequently occurring in various areas, and they could be mathematically abstracted as points residing in a geometric simplex. A proper distance metric on this simplex is of importance in many applications including classification and information retrieval. In this paper, we develop a novel framework to learn an optimal metric on the simplex. Ma jor features of our approach include: 1) its flexibility to handle correlations among bins/dimensions; 2) widespread applicability without being limited to ad hoc backgrounds; and 3) a "real" global solution in contrast to existing traditional local approaches. The technical essence of our approach is to fit a parametric distribution to the observed empirical data in the simplex. The distribution is parameterized by affinities between simplex vertices, which is learned via maximizing likelihood of observed data. Then, these affinities induce a metric on the simplex, defined as the earth mover's distance equipped with ground distances derived from simplex vertex affinities. |
| 关 键 词: | 比例数据; 参数分布; 信息检索 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-05：lxf |
| 阅读次数: | 50 |