图 书 馆
信息论度量学习Information-Theoretic Metric Learning |
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| 课程网址: | http://videolectures.net/lce06_davis_itml/ |
| 主讲教师: | Jason Davis |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the Mahalanobis distance function. Via a surprising equivalence, we show that this problem can be solved as a low-rank kernel learning problem. Specifically, we minimize the Burg divergence of a low-rank kernel to an input kernel, subject to pairwise distance constraints. Our approach has several advantages over existing methods. First, we present a natural information-theoretic formulation for the problem. Second, the algorithm utilizes the methods developed by Kulis et al. [6], which do not involve any eigenvector computation; in particular, the running time of our method is faster than most existing techniques. Third, the formulation offers insights into connections between metric learning and kernel learning. |
| 关 键 词: | 度量学习; 距离函数; 微分相对熵 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-12：王勇彬(课程编辑志愿者) |
| 阅读次数: | 290 |