图 书 馆
基于Dirichlet过程先验的多任务压缩感知Multi-Task Compressive Sensing with Dirichlet Process Priors |
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| 课程网址: | http://videolectures.net/icml08_carin_mtcs/ |
| 主讲教师: | Lawrence Carin |
| 开课单位: | 杜克大学 |
| 开课时间: | 2008-08-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Compressive sensing (CS) is an emerging field that, under appropriate conditions, can significantly reduce the number of measurements required for a given signal. In many applications, one is interested in multiple signals that may be measured in multiple CS-type measurements, where here each signal corresponds to a sensing "task". In this paper we propose a novel multi-task compressive sensing framework based on a Bayesian formalism, where a Dirichlet process (DP) prior is employed, yielding a principled means of simultaneously inferring the appropriate sharing mechanisms as well as CS inversion for each task. A variational Bayesian (VB) inference algorithm is employed to estimate the full posterior on the model parameters. |
| 关 键 词: | 压缩感知; 贝叶斯; 共享机制 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-17：lxf |
| 阅读次数: | 95 |