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冗余位向量搜索高维区域Redundant Bit Vectors for Searching High-Dimensional Regions |
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| 课程网址: | http://videolectures.net/mlws04_platt_rbvsh/ |
| 主讲教师: | John Platt |
| 开课单位: | 微软公司 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Many multimedia applications reduce to the problem of searching a database of high-dimensional regions to see whether any overlap a query point. There is a large literature of indexing techniques based on trees, all of which break down given high enough dimension of stored regions. We have created a new data structure, called redundant bit vectors (RBVs), that can effectively index high-dimensional regions.Using RBVs, we can search a database of 240K 64-dimensional hyperspheres, each with a different radius, up to 56 times faster than an optimized learning scan. RBVs are general-purpose, and may be useful for machine learning applications. |
| 关 键 词: | 高维区域; 数据结构; 冗余位向量; RBV |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 56 |