图 书 馆
低阶建模Low-rank modeling |
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| 课程网址: | http://videolectures.net/mlss2011_candes_lowrank/ |
| 主讲教师: | Emmanuel Candes |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2011-10-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Inspired by the success of compressive sensing, the last three years have seen an explosion of research in the theory of low-rank modeling. By now, we have results stating that it is possible to recover certain low-rank matrices from a minimal number of entries -- or of linear functionals -- by tractable convex optimization. We further know that these methods are robust vis a vis additive noise and even outliers. In a different direction, researchers have developed computationally tractable methods for clustering high-dimensional data points that are assumed to be drawn from multiple low-dimensional linear subspaces. This talk will survey some exciting results in these areas. |
| 关 键 词: | 压缩传感; 低等级建模; 低秩矩阵 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-23：cwx |
| 阅读次数: | 78 |