图 书 馆
压缩传感，通过L1最小化稀疏信号恢复概述An Overview of Compressed Sensing and Sparse Signal Recovery via L1 Minimization |
|
| 课程网址: | http://videolectures.net/mlss09us_candes_ocsssrl1m/ |
| 主讲教师: | Emmanuel Candes |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2009-07-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In many applications, one often has fewer equations than unknowns. While this seems hopeless, the premise that the object we wish to recover is sparse or nearly sparse radically changes the problem, making the search for solutions feasible. This lecture will introduce sparsity as a key modeling tool together with a series of little miracles touching on many areas of data processing. These examples show that finding *that* solution to an underdetermined system of linear equations with minimum L1 norm, often returns the ''right'' answer. Further, there is by now a well-established body of work going by the name of compressed sensing, which asserts that one can exploit sparsity or compressibility when acquiring signals of general interest, and that one can design nonadaptive sampling techniques that condense the information in a compressible signal into a small amount of data - in fewer data points than were thought necessary. We will survey some of these theories and trace back some of their origins to early work done in the 50's. Because these theories are broadly applicable in nature, the tutorial will move through several applications areas that may be impacted such as signal processing, bio-medical imaging, machine learning and so on. Finally, we will discuss how these theories and methods have far reaching implications for sensor design and other types of designs. |
| 关 键 词: | 机器学习; 生物医学成像; 建模 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-29：zyk |
| 阅读次数: | 52 |