图 书 馆
通用多维标度Universal Multi-Dimensional Scaling |
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| 课程网址: | http://videolectures.net/kdd2010_agarwal_umd/ |
| 主讲教师: | Arvind Agarwal |
| 开课单位: | 犹他大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper, we propose a unified algorithmic framework for solving many known variants of MDS. Our algorithm is a simple iterative scheme with guaranteed convergence, and is modular; by changing the internals of a single subroutine in the algorithm, we can switch cost functions and target spaces easily. In addition to the formal guarantees of convergence, our algorithms are accurate; in most cases, they converge to better quality solutions than existing methods in comparable time. Moreover, they have a small memory footprint and scale effectively for large data sets. We expect that this framework will be useful for a number of MDS variants that have not yet been studied. Our framework extends to embedding high-dimensional points lying on a sphere to points on a lower dimensional sphere, preserving geodesic distances. As a complement to this result, we also extend the Johnson-Lindenstrauss Lemma to this spherical setting, by showing that projecting to a random O((1/eps2) log n)-dimensional sphere causes only an eps-distortion in the geodesic distances. |
| 关 键 词: | 算法框架; 迭代方案; 模块化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-10：cwx |
| 阅读次数: | 56 |