图 书 馆
远程度量学习的快速求解和有效实现Fast Solvers and Efficient Implementations for Distance Metric Learning |
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| 课程网址: | http://videolectures.net/icml08_weinberger_fse/ |
| 主讲教师: | Kilian Weinberger |
| 开课单位: | 康奈尔大学 |
| 开课时间: | 2008-08-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper we study how to improve nearest neighbor classification by learning a Mahalanobis distance metric. We build on a recently proposed framework for distance metric learning known as large margin nearest neighbor (LMNN) classification. Within this framework, we focus specifically on the challenges in scalability and adaptability posed by large data sets. Our paper makes three contributions. First, we describe a highly efficient solver for the particular instance of semidefinite programming that arises in LMNN classification; our solver can handle problems with billions of large margin constraints in a few hours. Second, we show how to reduce both training and testing times using metric ball trees; the speedups from ball trees are further magnified by learning low dimensional representations of the input space. Third, we show how to learn different Mahalanobis distance metrics in different parts of the input space. For large data sets, these mixtures of locally adaptive metrics lead to even lower error rates. |
| 关 键 词: | 最近邻分类; 数据集; 求解器 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-21：lxf |
| 阅读次数: | 97 |