图 书 馆
蒸馏感测：主动感测，稀疏恢复Distilled Sensing: Active sensing for sparse recovery |
|
| 课程网址: | http://videolectures.net/smls09_castro_dsasf/ |
| 主讲教师: | Rui Castro |
| 开课单位: | 哥伦比亚大学 |
| 开课时间: | 2009-05-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The study and use of sparse representations in data-rich applications has garnered signi cant attention in the signal processing, statistics, and machine learning communities. In the present work we describe a novel sensing procedure called Distilled Sensing (DS), which is a sequential and adaptive approach for recovering sparse signals in noise. Passive sensing approaches, currently the most widespread data collection methods, involve non- adaptive data collection procedures that are completely speci ed before any data is observed. In contrast, DS collects data in a sequential and adaptive manner. Often such procedures are known as active sensing or sequential experimental design, and allow the use of data observed in earlier stages to guide the collection of future data. The added exibility of active sensing, together with a sparsity assumption, has the potential to enable extremely effcient and accurate inference. |
| 关 键 词: | 稀疏表示; 信号处理; 机器学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-21：cwx |
| 阅读次数: | 38 |