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最佳论文 - 信息理论度量学习Best Paper - Information-Theoretic Metric Learning |
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| 课程网址: | http://videolectures.net/icml07_kulis_itml/ |
| 主讲教师: | Brian Kulis |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2007-06-22 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the distance function. We express this problem as a particular Bregman optimization problem: that of minimizing the LogDet divergence subject to linear constraints. Our resulting algorithm has several advantages over existing methods. First, our method can handle a wide variety of constraints and can optionally incorporate a prior on the distance function. Second, it is fast and scalable. Unlike most existing methods, no eigenvalue computations or semi-definite programming are required. We also present an online version and derive regret bounds for the resulting algorithm. Finally, we evaluate our method on a recent error reporting system for software called Clarify, in the context of metric learning for nearest neighbor classification, as well as on standard data sets. |
| 关 键 词: | 距离函数; 多元高斯; 微分相对熵 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-15：heyf |
| 阅读次数: | 204 |