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压缩感知的共轭梯度迭代硬阈值分割与矩阵完备化Conjugate gradient iterative hard thresholding for compressed sensing and matrix completion |
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| 课程网址: | http://videolectures.net/sahd2014_tanner_compressed_sensing/ |
| 主讲教师: | Jared Tanner |
| 开课单位: | 牛津大学 |
| 开课时间: | 2014-10-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Compressed sensing and matrix completion are techniques by which simplicity in data can be exploited for more efficient data acquisition. For instance, if a matrix is known to be (approximately) low rank then it can be recovered from few of its entries. The design and analysis of computationally efficient algorithms for these problems has been extensively studies over the last 8 years. In this talk we present a new algorithm that balances low per iteration complexity with fast asymptotic convergence. This algorithm has been shown to have faster recovery time than any other known algorithm in the area, both for small scale problems and massively parallel GPU implementations. The new algorithm adapts the classical nonlinear conjugate gradient algorithm and shows the efficacy of a linear algebra perspective to compressed sensing and matrix completion. |
| 关 键 词: | 矩阵; 共轭梯度; 线性代数 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-07：yxd |
| 最后编审: | 2020-12-07：yxd |
| 阅读次数: | 24 |